{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:28.672807Z",
     "start_time": "2024-03-30T06:45:27.862306Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/home/loong/jupyter\")\n",
    "import common_utils\n",
    "from common_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载基础数据和applist特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:37.934418Z",
     "start_time": "2024-03-30T06:45:35.639574Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load 294 file,data shape (37254, 26)\n",
      "(37254, 26)\n"
     ]
    }
   ],
   "source": [
    "# 加载样本信息\n",
    "file_paths = data_of_dir(\"raw_012_am240327\",['_1_','_2_']) # type: ignore\n",
    "raw_df = batch_load_data(file_paths)\n",
    "print(raw_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:37.958190Z",
     "start_time": "2024-03-30T06:45:37.935951Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35739, 138)\n"
     ]
    }
   ],
   "source": [
    "# 加载applist的相关特征\n",
    "applist_variable_df = pd.read_parquet('applist_features240326.parquet')\n",
    "applist_features = applist_variable_df.columns.to_list()[1:]\n",
    "print(applist_variable_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载urule数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:48.923609Z",
     "start_time": "2024-03-30T06:45:37.959136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "发生异常，报错信息如下: \n",
      "Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\n",
      "异常的文件为:/home/loong/jupyter/YuChuang/urule_log/data/AM_type1_2024-02-06.parquet\n",
      "发生异常，报错信息如下: \n",
      "Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\n",
      "异常的文件为:/home/loong/jupyter/YuChuang/urule_log/data/AM_type1_2024-03-21.parquet\n",
      "发生异常，报错信息如下: \n",
      "Could not open Parquet input source '<Buffer>': Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.\n",
      "异常的文件为:/home/loong/jupyter/YuChuang/urule_log/data/AM_type1_2024-03-23.parquet\n",
      "load 152 file,data shape (136553, 8)\n",
      "(136553, 8)\n",
      "136553\n"
     ]
    }
   ],
   "source": [
    "# 加载决策引擎urule的日志信息\n",
    "urule_files = data_of_dir(\"/home/loong/jupyter/YuChuang/urule_log/data\",'AM_type1')\n",
    "urule_df = batch_load_data(urule_files)\n",
    "urule_df = urule_df.rename(columns={'create_time':'urule_time'})\n",
    "print(urule_df.shape)\n",
    "print(urule_df['tx_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:48.928181Z",
     "start_time": "2024-03-30T06:45:48.925328Z"
    }
   },
   "outputs": [],
   "source": [
    "urule_features = \"\"\"\n",
    "creditAvgAmount\n",
    "endBillsCreditAvgAmount\n",
    "hasGravitySensor\n",
    "hasGyroscopeSensor\n",
    "last1CreditAmount\n",
    "last1EndBillsCreditAmount\n",
    "last1LoanBillsCreditAmount\n",
    "last1LoanBillsLoanAmount\n",
    "last1OverdueBillsCreditAmount\n",
    "firstUseAndRequestIntervalTime\n",
    "last1OverdueBillsLoanAmount\n",
    "lastEndRepayDeltaDays\n",
    "lastWaitApplyDeltaDays\n",
    "lastWaitLoanDeltaDays\n",
    "lastWaitRepayDeltaDays\n",
    "loanBillsCreditAvgAmount\n",
    "loanBillsLoanAvgAmount\n",
    "loginAccountEnterTime\n",
    "overdueBillsCreditAvgAmount\n",
    "overdueBillsLoanAvgAmount\n",
    "refereeAffLoanMobEqAppCnt\n",
    "refereeAffLoanPhoneMobEqAppCnt\n",
    "refereeLast1MaxEndBillsOverdueDays\n",
    "refereeLast1MaxOverdueDays\n",
    "refereeLast2MaxEndBillsOverdueDays\n",
    "refereeLast2MaxOverdueDays\n",
    "refereeLast3MaxEndBillsOverdueDays\n",
    "refereeLast3MaxOverdueDays\n",
    "refereeLast4MaxEndBillsOverdueDays\n",
    "refereeLast4MaxOverdueDays\n",
    "refereeLast5MaxEndBillsOverdueDays\n",
    "refereeLast5MaxOverdueDays\n",
    "refereeMaxEndBillsOverdueDays\n",
    "refereeMaxOverdueDays\n",
    "refereeMaxWaitBillsOverdueDays\n",
    "faceBlackListMaxScore\n",
    "lastEndFinishDeltaDays\n",
    "endBillsLoanAvgAmount\n",
    "last1EndBillsLoanAmount\n",
    "last1LoanAmount\n",
    "loanAvgAmount\n",
    "educationLevel\n",
    "batteryPower\n",
    "screenBrightness\n",
    "readPrivacyAgreementTime\n",
    "applyHour\n",
    "graphDniBanknumberCount\n",
    "graphMobileBanknumberCount\n",
    "deviceApplyCnt\n",
    "lastEndApplyDeltaDays\n",
    "lastEndLoanDeltaDays\n",
    "batteryMax\n",
    "gender\n",
    "graphBanknumberDniCount\n",
    "graphBanknumberMobileCount\n",
    "graphDniMobileCount\n",
    "graphMobileDeviceidCount\n",
    "graphMobileDniCount\n",
    "backgroundRecoveryTimes\n",
    "endBillsCnt\n",
    "loanCnt\n",
    "readPrivacyAgreementTimes\n",
    "graphMobileGaidCount\n",
    "isRoot\n",
    "last1AvgEndBillsOverdueDays\n",
    "last1AvgOverdueDays\n",
    "last1MaxEndBillsOverdueDays\n",
    "last1MaxOverdueDays\n",
    "lastAllAvgEndBillsOverdueDays\n",
    "lastAllAvgOverdueDays\n",
    "lastAllMaxEndBillsOverdueDays\n",
    "lastAllMaxOverdueDays\n",
    "loanPageStayTime\n",
    "overdueBillsCnt\n",
    "overdueEndBillsCnt\n",
    "overdueProportion\n",
    "overdueWaitBillsCnt\n",
    "refereeAvgHoldPeriod\n",
    "refereeAvgLoanAffiliateProCnt\n",
    "refereeAvgRegisterAffiliateAppCnt\n",
    "refereeBillsCnt\n",
    "refereeEndBillsCnt\n",
    "refereeHitInternalBlCnt\n",
    "refereeLast1AvgHoldPeriod\n",
    "refereeLast2AvgHoldPeriod\n",
    "refereeLast3AvgHoldPeriod\n",
    "refereeLast4AvgHoldPeriod\n",
    "refereeLast5AvgHoldPeriod\n",
    "refereeOverdueBillsCnt\n",
    "refereeOverdueEndBillsCnt\n",
    "refereeOverdueProportion\n",
    "refereeOverdueWaitBillsCnt\n",
    "refereeWaitBillsCnt\n",
    "\"\"\"\n",
    "urule_features = urule_features.strip().split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:48.966832Z",
     "start_time": "2024-03-30T06:45:48.929827Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36355, 29)\n",
      "36355\n"
     ]
    }
   ],
   "source": [
    "merged_df = raw_df.merge(urule_df[['tx_id','req_data','resp_data','urule_time']],on = 'tx_id' )\n",
    "print(merged_df.shape)\n",
    "print(merged_df['tx_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:45:48.973352Z",
     "start_time": "2024-03-30T06:45:48.967901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 30)\n"
     ]
    }
   ],
   "source": [
    "merged_df['u_create_time'] = merged_df['urule_time']-pd.Timedelta(hours=14)\n",
    "condition = merged_df['loan_time'] <= merged_df['u_create_time']\n",
    "print(merged_df[condition].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:05.549268Z",
     "start_time": "2024-03-30T06:45:48.974486Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_filed_num(x):\n",
    "    try:\n",
    "        obj = json.loads(x)\n",
    "        return len(obj)\n",
    "    except Exception as e:\n",
    "        print(x)\n",
    "\n",
    "merged_df['urule_field_num'] = merged_df['req_data'].map(get_filed_num)\n",
    "merged_df[urule_features] = merged_df.apply(lambda x : query_json(x['req_data'],urule_features),axis=1,result_type='expand')\n",
    "merged_df[urule_features] = merged_df[urule_features].astype(float).fillna(-999.0).round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:08.720737Z",
     "start_time": "2024-03-30T06:46:05.550157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36355, 124)\n",
      "36316\n",
      "(36355, 124)\n"
     ]
    }
   ],
   "source": [
    "col_names = ['tx_id','req_data'] + urule_features\n",
    "print(merged_df.shape)\n",
    "condition = merged_df['loan_time'] > merged_df['u_create_time']\n",
    "print(condition.sum())\n",
    "merged_df[condition][col_names].to_parquet('raw_urule_df0327.parquet',compression='zstd')\n",
    "print(merged_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 合并数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:15.853265Z",
     "start_time": "2024-03-30T06:46:15.671447Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37254, 26)\n",
      "(35739, 163)\n",
      "(34813, 257)\n"
     ]
    }
   ],
   "source": [
    "print(raw_df.shape)\n",
    "data_df = raw_df.merge(applist_variable_df,on='app_order_id')\n",
    "print(data_df.shape)\n",
    "data_df = data_df.merge(merged_df[condition][col_names],on= 'tx_id')\n",
    "print(data_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:33.397958Z",
     "start_time": "2024-03-30T06:46:33.394946Z"
    }
   },
   "outputs": [],
   "source": [
    "applist_features = applist_variable_df.columns.to_list()[1:]\n",
    "urule_features = urule_features\n",
    "model_features = applist_features + urule_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备入模变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:35.142843Z",
     "start_time": "2024-03-30T06:46:35.123898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "    app                                id         phone  score system  \\\n0  AIMX               1177796036631384064  525585648411     97     AM   \n1   PRC  1e03caea772d49bdaa02f17d6e628265  523319997716     96      M   \n2   PRC  8d9c247d76db403889286ad07e94fe5d  526692477798     96      M   \n3   PAF  355acae192584f5a8e4f9a421f2eaf21  526271480680     96      M   \n4   CPO  eec09f4d5f0248a0bb0c5c4c6776b3a1  526631512253     96     YM   \n\n  phone_number          tag  \n0   5585648411  fraud_phone  \n1   3319997716  fraud_phone  \n2   6692477798  fraud_phone  \n3   6271480680  fraud_phone  \n4   6631512253  fraud_phone  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>app</th>\n      <th>id</th>\n      <th>phone</th>\n      <th>score</th>\n      <th>system</th>\n      <th>phone_number</th>\n      <th>tag</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AIMX</td>\n      <td>1177796036631384064</td>\n      <td>525585648411</td>\n      <td>97</td>\n      <td>AM</td>\n      <td>5585648411</td>\n      <td>fraud_phone</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PRC</td>\n      <td>1e03caea772d49bdaa02f17d6e628265</td>\n      <td>523319997716</td>\n      <td>96</td>\n      <td>M</td>\n      <td>3319997716</td>\n      <td>fraud_phone</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PRC</td>\n      <td>8d9c247d76db403889286ad07e94fe5d</td>\n      <td>526692477798</td>\n      <td>96</td>\n      <td>M</td>\n      <td>6692477798</td>\n      <td>fraud_phone</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PAF</td>\n      <td>355acae192584f5a8e4f9a421f2eaf21</td>\n      <td>526271480680</td>\n      <td>96</td>\n      <td>M</td>\n      <td>6271480680</td>\n      <td>fraud_phone</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CPO</td>\n      <td>eec09f4d5f0248a0bb0c5c4c6776b3a1</td>\n      <td>526631512253</td>\n      <td>96</td>\n      <td>YM</td>\n      <td>6631512253</td>\n      <td>fraud_phone</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取大胡子的信息\n",
    "huzi_df =[]\n",
    "with open('大胡子.txt','r') as file:\n",
    "    lines = file.readlines()\n",
    "for line in lines:\n",
    "    line = line.replace(\"'\",'\"').replace(\"\\n\",'')\n",
    "    huzi_df.append(json.loads(line))\n",
    "huzi_df = pd.DataFrame.from_records(huzi_df)\n",
    "huzi_df['phone_number'] = huzi_df['phone'].map(lambda x: x[2:])\n",
    "huzi_df['tag'] = 'fraud_phone'\n",
    "huzi_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:38.367701Z",
     "start_time": "2024-03-30T06:46:36.608634Z"
    }
   },
   "outputs": [],
   "source": [
    "data_df['def_pd1'] = data_df.apply(def_pd1_aclc,axis=1) # type: ignore\n",
    "data_df['def_cpd'] = data_df.apply(def_cpd_aclc,axis=1) # type: ignore\n",
    "data_df[\"apply_week\"] = data_df[\"apply_time\"].map(format_date2week)\n",
    "data_df[\"loan_week\"] = data_df[\"loan_time\"].map(format_date2week)\n",
    "data_df[\"upload_date\"] = data_df[\"sms_upload_time\"].dt.strftime(\"%Y-%m-%d\")\n",
    "data_df[\"apply_date\"] = data_df[\"apply_time\"].dt.strftime(\"%Y-%m-%d\")\n",
    "data_df[\"loan_date\"] = data_df[\"loan_time\"].dt.strftime(\"%Y-%m-%d\")\n",
    "data_df[\"total\"] = \"ALL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:38.386125Z",
     "start_time": "2024-03-30T06:46:38.368934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                  def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id  \\\nloan_week                                                                     \n2023.10.30~11.05     12.0     27.0     12.0     27.0            27        8   \n2023.11.06~11.12     73.0    304.0     68.0    304.0           304       97   \n2023.11.13~11.19    265.0    872.0    248.0    872.0           872      254   \n2023.11.20~11.26    478.0   1463.0    445.0   1463.0          1463      393   \n2023.11.27~12.03    810.0   2239.0    746.0   2239.0          2239      418   \n2023.12.04~12.10   1043.0   2473.0    964.0   2473.0          2473      449   \n2023.12.11~12.17    571.0   1534.0    537.0   1534.0          1534      356   \n2023.12.18~12.24    598.0   1454.0    553.0   1454.0          1454      322   \n2023.12.25~12.31    652.0   1678.0    592.0   1678.0          1678      382   \n2024.01.01~01.07    594.0   1738.0    550.0   1738.0          1738      402   \n2024.01.08~01.14    598.0   1723.0    543.0   1723.0          1723      437   \n2024.01.15~01.21    740.0   1927.0    683.0   1927.0          1927      458   \n2024.01.22~01.28    674.0   1783.0    587.0   1783.0          1783      455   \n2024.01.29~02.04    809.0   2110.0    723.0   2110.0          2110      457   \n2024.02.05~02.11    705.0   1561.0    633.0   1561.0          1561      379   \n2024.02.12~02.18    708.0   1532.0    623.0   1532.0          1532      317   \n2024.02.19~02.25    671.0   1822.0    626.0   1822.0          1822      412   \n2024.02.26~03.03    754.0   2134.0    691.0   2134.0          2134      473   \n2024.03.04~03.10    815.0   2163.0    757.0   2163.0          2163      508   \n2024.03.11~03.17    970.0   2300.0    919.0   2300.0          2300      515   \n2024.03.18~03.24    323.0    842.0    329.0    913.0          1264      377   \n2024.03.25~03.31      0.0      0.0      0.0      0.0           712      246   \n\n                       pd1       cpd  \nloan_week                             \n2023.10.30~11.05  0.444444  0.444444  \n2023.11.06~11.12  0.240132  0.223684  \n2023.11.13~11.19  0.303899  0.284404  \n2023.11.20~11.26  0.326726  0.304170  \n2023.11.27~12.03  0.361769  0.333184  \n2023.12.04~12.10  0.421755  0.389810  \n2023.12.11~12.17  0.372229  0.350065  \n2023.12.18~12.24  0.411279  0.380330  \n2023.12.25~12.31  0.388558  0.352801  \n2024.01.01~01.07  0.341772  0.316456  \n2024.01.08~01.14  0.347069  0.315148  \n2024.01.15~01.21  0.384017  0.354437  \n2024.01.22~01.28  0.378015  0.329220  \n2024.01.29~02.04  0.383412  0.342654  \n2024.02.05~02.11  0.451634  0.405509  \n2024.02.12~02.18  0.462141  0.406658  \n2024.02.19~02.25  0.368277  0.343578  \n2024.02.26~03.03  0.353327  0.323805  \n2024.03.04~03.10  0.376791  0.349977  \n2024.03.11~03.17  0.421739  0.399565  \n2024.03.18~03.24  0.383610  0.360350  \n2024.03.25~03.31       NaN       NaN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>loan_week</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023.10.30~11.05</th>\n      <td>12.0</td>\n      <td>27.0</td>\n      <td>12.0</td>\n      <td>27.0</td>\n      <td>27</td>\n      <td>8</td>\n      <td>0.444444</td>\n      <td>0.444444</td>\n    </tr>\n    <tr>\n      <th>2023.11.06~11.12</th>\n      <td>73.0</td>\n      <td>304.0</td>\n      <td>68.0</td>\n      <td>304.0</td>\n      <td>304</td>\n      <td>97</td>\n      <td>0.240132</td>\n      <td>0.223684</td>\n    </tr>\n    <tr>\n      <th>2023.11.13~11.19</th>\n      <td>265.0</td>\n      <td>872.0</td>\n      <td>248.0</td>\n      <td>872.0</td>\n      <td>872</td>\n      <td>254</td>\n      <td>0.303899</td>\n      <td>0.284404</td>\n    </tr>\n    <tr>\n      <th>2023.11.20~11.26</th>\n      <td>478.0</td>\n      <td>1463.0</td>\n      <td>445.0</td>\n      <td>1463.0</td>\n      <td>1463</td>\n      <td>393</td>\n      <td>0.326726</td>\n      <td>0.304170</td>\n    </tr>\n    <tr>\n      <th>2023.11.27~12.03</th>\n      <td>810.0</td>\n      <td>2239.0</td>\n      <td>746.0</td>\n      <td>2239.0</td>\n      <td>2239</td>\n      <td>418</td>\n      <td>0.361769</td>\n      <td>0.333184</td>\n    </tr>\n    <tr>\n      <th>2023.12.04~12.10</th>\n      <td>1043.0</td>\n      <td>2473.0</td>\n      <td>964.0</td>\n      <td>2473.0</td>\n      <td>2473</td>\n      <td>449</td>\n      <td>0.421755</td>\n      <td>0.389810</td>\n    </tr>\n    <tr>\n      <th>2023.12.11~12.17</th>\n      <td>571.0</td>\n      <td>1534.0</td>\n      <td>537.0</td>\n      <td>1534.0</td>\n      <td>1534</td>\n      <td>356</td>\n      <td>0.372229</td>\n      <td>0.350065</td>\n    </tr>\n    <tr>\n      <th>2023.12.18~12.24</th>\n      <td>598.0</td>\n      <td>1454.0</td>\n      <td>553.0</td>\n      <td>1454.0</td>\n      <td>1454</td>\n      <td>322</td>\n      <td>0.411279</td>\n      <td>0.380330</td>\n    </tr>\n    <tr>\n      <th>2023.12.25~12.31</th>\n      <td>652.0</td>\n      <td>1678.0</td>\n      <td>592.0</td>\n      <td>1678.0</td>\n      <td>1678</td>\n      <td>382</td>\n      <td>0.388558</td>\n      <td>0.352801</td>\n    </tr>\n    <tr>\n      <th>2024.01.01~01.07</th>\n      <td>594.0</td>\n      <td>1738.0</td>\n      <td>550.0</td>\n      <td>1738.0</td>\n      <td>1738</td>\n      <td>402</td>\n      <td>0.341772</td>\n      <td>0.316456</td>\n    </tr>\n    <tr>\n      <th>2024.01.08~01.14</th>\n      <td>598.0</td>\n      <td>1723.0</td>\n      <td>543.0</td>\n      <td>1723.0</td>\n      <td>1723</td>\n      <td>437</td>\n      <td>0.347069</td>\n      <td>0.315148</td>\n    </tr>\n    <tr>\n      <th>2024.01.15~01.21</th>\n      <td>740.0</td>\n      <td>1927.0</td>\n      <td>683.0</td>\n      <td>1927.0</td>\n      <td>1927</td>\n      <td>458</td>\n      <td>0.384017</td>\n      <td>0.354437</td>\n    </tr>\n    <tr>\n      <th>2024.01.22~01.28</th>\n      <td>674.0</td>\n      <td>1783.0</td>\n      <td>587.0</td>\n      <td>1783.0</td>\n      <td>1783</td>\n      <td>455</td>\n      <td>0.378015</td>\n      <td>0.329220</td>\n    </tr>\n    <tr>\n      <th>2024.01.29~02.04</th>\n      <td>809.0</td>\n      <td>2110.0</td>\n      <td>723.0</td>\n      <td>2110.0</td>\n      <td>2110</td>\n      <td>457</td>\n      <td>0.383412</td>\n      <td>0.342654</td>\n    </tr>\n    <tr>\n      <th>2024.02.05~02.11</th>\n      <td>705.0</td>\n      <td>1561.0</td>\n      <td>633.0</td>\n      <td>1561.0</td>\n      <td>1561</td>\n      <td>379</td>\n      <td>0.451634</td>\n      <td>0.405509</td>\n    </tr>\n    <tr>\n      <th>2024.02.12~02.18</th>\n      <td>708.0</td>\n      <td>1532.0</td>\n      <td>623.0</td>\n      <td>1532.0</td>\n      <td>1532</td>\n      <td>317</td>\n      <td>0.462141</td>\n      <td>0.406658</td>\n    </tr>\n    <tr>\n      <th>2024.02.19~02.25</th>\n      <td>671.0</td>\n      <td>1822.0</td>\n      <td>626.0</td>\n      <td>1822.0</td>\n      <td>1822</td>\n      <td>412</td>\n      <td>0.368277</td>\n      <td>0.343578</td>\n    </tr>\n    <tr>\n      <th>2024.02.26~03.03</th>\n      <td>754.0</td>\n      <td>2134.0</td>\n      <td>691.0</td>\n      <td>2134.0</td>\n      <td>2134</td>\n      <td>473</td>\n      <td>0.353327</td>\n      <td>0.323805</td>\n    </tr>\n    <tr>\n      <th>2024.03.04~03.10</th>\n      <td>815.0</td>\n      <td>2163.0</td>\n      <td>757.0</td>\n      <td>2163.0</td>\n      <td>2163</td>\n      <td>508</td>\n      <td>0.376791</td>\n      <td>0.349977</td>\n    </tr>\n    <tr>\n      <th>2024.03.11~03.17</th>\n      <td>970.0</td>\n      <td>2300.0</td>\n      <td>919.0</td>\n      <td>2300.0</td>\n      <td>2300</td>\n      <td>515</td>\n      <td>0.421739</td>\n      <td>0.399565</td>\n    </tr>\n    <tr>\n      <th>2024.03.18~03.24</th>\n      <td>323.0</td>\n      <td>842.0</td>\n      <td>329.0</td>\n      <td>913.0</td>\n      <td>1264</td>\n      <td>377</td>\n      <td>0.383610</td>\n      <td>0.360350</td>\n    </tr>\n    <tr>\n      <th>2024.03.25~03.31</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>712</td>\n      <td>246</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = [\"loan_week\"]\n",
    "sum_col = [\"def_pd1\", \"agr_pd1\", \"def_cpd\", \"agr_cpd\"]\n",
    "count_col = [\"app_order_id\"]\n",
    "unique_col = [\"user_id\"]\n",
    "rate_tupes = [(\"def_pd1\", \"agr_pd1\", \"pd1\"), (\"def_cpd\", \"agr_cpd\", \"cpd\")]\n",
    "group_calc(data_df, group, sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:38.722848Z",
     "start_time": "2024-03-30T06:46:38.712351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id       pd1  \\\ntotal                                                                        \nALL    12863.0  33679.0  11829.0  33750.0         34813     3818  0.381929   \n\n            cpd  \ntotal            \nALL    0.350489  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ALL</th>\n      <td>12863.0</td>\n      <td>33679.0</td>\n      <td>11829.0</td>\n      <td>33750.0</td>\n      <td>34813</td>\n      <td>3818</td>\n      <td>0.381929</td>\n      <td>0.350489</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_calc(data_df, 'total', sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:39.948914Z",
     "start_time": "2024-03-30T06:46:39.934659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7737, 263)\n"
     ]
    }
   ],
   "source": [
    "# 老客产品建模过程仅保留主产品\n",
    "condition = data_df['product_set_code'].isin(['AIMX', 'ATCC', 'ARIP'])  & (data_df['applist']!='') &  data_df['applist'].notnull() & (~data_df['phone_number'].isin(huzi_df['phone_number']))\n",
    "data_df_new = data_df[condition]\n",
    "print(data_df_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:40.566722Z",
     "start_time": "2024-03-30T06:46:40.552750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                  def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id  \\\nloan_week                                                                     \n2023.10.30~11.05      4.0      8.0      4.0      8.0             8        8   \n2023.11.06~11.12     24.0    101.0     22.0    101.0           101       90   \n2023.11.13~11.19     89.0    292.0     84.0    292.0           292      240   \n2023.11.20~11.26    115.0    392.0    108.0    392.0           392      325   \n2023.11.27~12.03    139.0    404.0    129.0    404.0           404      337   \n2023.12.04~12.10    171.0    414.0    152.0    414.0           414      347   \n2023.12.11~12.17    105.0    301.0     98.0    301.0           301      250   \n2023.12.18~12.24    126.0    311.0    114.0    311.0           311      269   \n2023.12.25~12.31    131.0    356.0    118.0    356.0           356      304   \n2024.01.01~01.07    112.0    335.0    108.0    335.0           335      277   \n2024.01.08~01.14     95.0    269.0     82.0    269.0           269      225   \n2024.01.15~01.21    138.0    359.0    123.0    359.0           359      254   \n2024.01.22~01.28    129.0    384.0    110.0    384.0           384      270   \n2024.01.29~02.04    180.0    485.0    158.0    485.0           485      319   \n2024.02.05~02.11    168.0    395.0    148.0    395.0           395      303   \n2024.02.12~02.18    171.0    376.0    149.0    376.0           376      269   \n2024.02.19~02.25    147.0    454.0    140.0    454.0           454      347   \n2024.02.26~03.03    177.0    498.0    160.0    498.0           498      375   \n2024.03.04~03.10    190.0    510.0    179.0    510.0           510      380   \n2024.03.11~03.17    232.0    550.0    224.0    550.0           550      412   \n2024.03.18~03.24     74.0    208.0     82.0    230.0           330      267   \n2024.03.25~03.31      0.0      0.0      0.0      0.0           213      192   \n\n                       pd1       cpd  \nloan_week                             \n2023.10.30~11.05  0.500000  0.500000  \n2023.11.06~11.12  0.237624  0.217822  \n2023.11.13~11.19  0.304795  0.287671  \n2023.11.20~11.26  0.293367  0.275510  \n2023.11.27~12.03  0.344059  0.319307  \n2023.12.04~12.10  0.413043  0.367150  \n2023.12.11~12.17  0.348837  0.325581  \n2023.12.18~12.24  0.405145  0.366559  \n2023.12.25~12.31  0.367978  0.331461  \n2024.01.01~01.07  0.334328  0.322388  \n2024.01.08~01.14  0.353160  0.304833  \n2024.01.15~01.21  0.384401  0.342618  \n2024.01.22~01.28  0.335938  0.286458  \n2024.01.29~02.04  0.371134  0.325773  \n2024.02.05~02.11  0.425316  0.374684  \n2024.02.12~02.18  0.454787  0.396277  \n2024.02.19~02.25  0.323789  0.308370  \n2024.02.26~03.03  0.355422  0.321285  \n2024.03.04~03.10  0.372549  0.350980  \n2024.03.11~03.17  0.421818  0.407273  \n2024.03.18~03.24  0.355769  0.356522  \n2024.03.25~03.31       NaN       NaN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>loan_week</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2023.10.30~11.05</th>\n      <td>4.0</td>\n      <td>8.0</td>\n      <td>4.0</td>\n      <td>8.0</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>2023.11.06~11.12</th>\n      <td>24.0</td>\n      <td>101.0</td>\n      <td>22.0</td>\n      <td>101.0</td>\n      <td>101</td>\n      <td>90</td>\n      <td>0.237624</td>\n      <td>0.217822</td>\n    </tr>\n    <tr>\n      <th>2023.11.13~11.19</th>\n      <td>89.0</td>\n      <td>292.0</td>\n      <td>84.0</td>\n      <td>292.0</td>\n      <td>292</td>\n      <td>240</td>\n      <td>0.304795</td>\n      <td>0.287671</td>\n    </tr>\n    <tr>\n      <th>2023.11.20~11.26</th>\n      <td>115.0</td>\n      <td>392.0</td>\n      <td>108.0</td>\n      <td>392.0</td>\n      <td>392</td>\n      <td>325</td>\n      <td>0.293367</td>\n      <td>0.275510</td>\n    </tr>\n    <tr>\n      <th>2023.11.27~12.03</th>\n      <td>139.0</td>\n      <td>404.0</td>\n      <td>129.0</td>\n      <td>404.0</td>\n      <td>404</td>\n      <td>337</td>\n      <td>0.344059</td>\n      <td>0.319307</td>\n    </tr>\n    <tr>\n      <th>2023.12.04~12.10</th>\n      <td>171.0</td>\n      <td>414.0</td>\n      <td>152.0</td>\n      <td>414.0</td>\n      <td>414</td>\n      <td>347</td>\n      <td>0.413043</td>\n      <td>0.367150</td>\n    </tr>\n    <tr>\n      <th>2023.12.11~12.17</th>\n      <td>105.0</td>\n      <td>301.0</td>\n      <td>98.0</td>\n      <td>301.0</td>\n      <td>301</td>\n      <td>250</td>\n      <td>0.348837</td>\n      <td>0.325581</td>\n    </tr>\n    <tr>\n      <th>2023.12.18~12.24</th>\n      <td>126.0</td>\n      <td>311.0</td>\n      <td>114.0</td>\n      <td>311.0</td>\n      <td>311</td>\n      <td>269</td>\n      <td>0.405145</td>\n      <td>0.366559</td>\n    </tr>\n    <tr>\n      <th>2023.12.25~12.31</th>\n      <td>131.0</td>\n      <td>356.0</td>\n      <td>118.0</td>\n      <td>356.0</td>\n      <td>356</td>\n      <td>304</td>\n      <td>0.367978</td>\n      <td>0.331461</td>\n    </tr>\n    <tr>\n      <th>2024.01.01~01.07</th>\n      <td>112.0</td>\n      <td>335.0</td>\n      <td>108.0</td>\n      <td>335.0</td>\n      <td>335</td>\n      <td>277</td>\n      <td>0.334328</td>\n      <td>0.322388</td>\n    </tr>\n    <tr>\n      <th>2024.01.08~01.14</th>\n      <td>95.0</td>\n      <td>269.0</td>\n      <td>82.0</td>\n      <td>269.0</td>\n      <td>269</td>\n      <td>225</td>\n      <td>0.353160</td>\n      <td>0.304833</td>\n    </tr>\n    <tr>\n      <th>2024.01.15~01.21</th>\n      <td>138.0</td>\n      <td>359.0</td>\n      <td>123.0</td>\n      <td>359.0</td>\n      <td>359</td>\n      <td>254</td>\n      <td>0.384401</td>\n      <td>0.342618</td>\n    </tr>\n    <tr>\n      <th>2024.01.22~01.28</th>\n      <td>129.0</td>\n      <td>384.0</td>\n      <td>110.0</td>\n      <td>384.0</td>\n      <td>384</td>\n      <td>270</td>\n      <td>0.335938</td>\n      <td>0.286458</td>\n    </tr>\n    <tr>\n      <th>2024.01.29~02.04</th>\n      <td>180.0</td>\n      <td>485.0</td>\n      <td>158.0</td>\n      <td>485.0</td>\n      <td>485</td>\n      <td>319</td>\n      <td>0.371134</td>\n      <td>0.325773</td>\n    </tr>\n    <tr>\n      <th>2024.02.05~02.11</th>\n      <td>168.0</td>\n      <td>395.0</td>\n      <td>148.0</td>\n      <td>395.0</td>\n      <td>395</td>\n      <td>303</td>\n      <td>0.425316</td>\n      <td>0.374684</td>\n    </tr>\n    <tr>\n      <th>2024.02.12~02.18</th>\n      <td>171.0</td>\n      <td>376.0</td>\n      <td>149.0</td>\n      <td>376.0</td>\n      <td>376</td>\n      <td>269</td>\n      <td>0.454787</td>\n      <td>0.396277</td>\n    </tr>\n    <tr>\n      <th>2024.02.19~02.25</th>\n      <td>147.0</td>\n      <td>454.0</td>\n      <td>140.0</td>\n      <td>454.0</td>\n      <td>454</td>\n      <td>347</td>\n      <td>0.323789</td>\n      <td>0.308370</td>\n    </tr>\n    <tr>\n      <th>2024.02.26~03.03</th>\n      <td>177.0</td>\n      <td>498.0</td>\n      <td>160.0</td>\n      <td>498.0</td>\n      <td>498</td>\n      <td>375</td>\n      <td>0.355422</td>\n      <td>0.321285</td>\n    </tr>\n    <tr>\n      <th>2024.03.04~03.10</th>\n      <td>190.0</td>\n      <td>510.0</td>\n      <td>179.0</td>\n      <td>510.0</td>\n      <td>510</td>\n      <td>380</td>\n      <td>0.372549</td>\n      <td>0.350980</td>\n    </tr>\n    <tr>\n      <th>2024.03.11~03.17</th>\n      <td>232.0</td>\n      <td>550.0</td>\n      <td>224.0</td>\n      <td>550.0</td>\n      <td>550</td>\n      <td>412</td>\n      <td>0.421818</td>\n      <td>0.407273</td>\n    </tr>\n    <tr>\n      <th>2024.03.18~03.24</th>\n      <td>74.0</td>\n      <td>208.0</td>\n      <td>82.0</td>\n      <td>230.0</td>\n      <td>330</td>\n      <td>267</td>\n      <td>0.355769</td>\n      <td>0.356522</td>\n    </tr>\n    <tr>\n      <th>2024.03.25~03.31</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>213</td>\n      <td>192</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = [\"loan_week\"]\n",
    "sum_col = [\"def_pd1\", \"agr_pd1\", \"def_cpd\", \"agr_cpd\"]\n",
    "count_col = [\"app_order_id\"]\n",
    "unique_col = [\"user_id\"]\n",
    "rate_tupes = [(\"def_pd1\", \"agr_pd1\", \"pd1\"), (\"def_cpd\", \"agr_cpd\", \"cpd\")]\n",
    "group_calc(data_df_new, group, sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:41.237252Z",
     "start_time": "2024-03-30T06:46:41.226077Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id       pd1  \\\ntotal                                                                        \nALL     2717.0   7402.0   2492.0   7424.0          7737     3306  0.367063   \n\n            cpd  \ntotal            \nALL    0.335668  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ALL</th>\n      <td>2717.0</td>\n      <td>7402.0</td>\n      <td>2492.0</td>\n      <td>7424.0</td>\n      <td>7737</td>\n      <td>3306</td>\n      <td>0.367063</td>\n      <td>0.335668</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_calc(data_df_new, 'total', sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:42.164599Z",
     "start_time": "2024-03-30T06:46:42.153061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "             def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id  \\\nacq_channel                                                              \nAIMX          2278.0   6258.0   2098.0   6277.0          6532     3123   \nATCC           439.0   1144.0    394.0   1147.0          1205      669   \n\n                  pd1       cpd  \nacq_channel                      \nAIMX         0.364014  0.334236  \nATCC         0.383741  0.343505  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>acq_channel</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AIMX</th>\n      <td>2278.0</td>\n      <td>6258.0</td>\n      <td>2098.0</td>\n      <td>6277.0</td>\n      <td>6532</td>\n      <td>3123</td>\n      <td>0.364014</td>\n      <td>0.334236</td>\n    </tr>\n    <tr>\n      <th>ATCC</th>\n      <td>439.0</td>\n      <td>1144.0</td>\n      <td>394.0</td>\n      <td>1147.0</td>\n      <td>1205</td>\n      <td>669</td>\n      <td>0.383741</td>\n      <td>0.343505</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_calc(data_df_new, 'acq_channel', sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分train test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:46:43.865903Z",
     "start_time": "2024-03-30T06:46:43.849511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3306\n",
      "2314\n",
      "992\n",
      "(5228, 263) 2218\n",
      "(2174, 263) 950\n",
      "(2100, 263) 1051\n",
      "(335, 263) 276\n"
     ]
    }
   ],
   "source": [
    "unqiue_user = data_df_new['user_id'].unique()\n",
    "np.random.seed(42)\n",
    "train_size = int(len(unqiue_user)*0.7)\n",
    "train_user = np.random.choice(unqiue_user,size=train_size,replace=False)\n",
    "test_user = np.setdiff1d(unqiue_user,train_user)\n",
    "print(len(unqiue_user))\n",
    "print(len(train_user))\n",
    "print(len(test_user))\n",
    "train = data_df_new[(data_df_new.agr_pd1==1)&(data_df_new.user_id.isin(train_user))]\n",
    "test = data_df_new[(data_df_new.agr_pd1==1)&(data_df_new.user_id.isin(test_user))]\n",
    "oot = data_df_new[(data_df_new.agr_pd1==1)&(data_df_new.loan_date>'2024-02-20')]\n",
    "oot2 = data_df_new[(data_df_new.loan_date>'2024-02-20')&(data_df_new.agr_pd1!=1)]\n",
    "print(train.shape,train['user_id'].nunique())\n",
    "print(test.shape,test['user_id'].nunique())\n",
    "print(oot.shape,oot['user_id'].nunique())\n",
    "print(oot2.shape,oot2['user_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:20:49.836707Z",
     "start_time": "2024-03-28T12:20:49.826730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id       pd1  \\\ntotal                                                                        \nALL     1875.0   5228.0   1717.0   5228.0          5228     2218  0.358646   \n\n            cpd  \ntotal            \nALL    0.328424  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ALL</th>\n      <td>1875.0</td>\n      <td>5228.0</td>\n      <td>1717.0</td>\n      <td>5228.0</td>\n      <td>5228</td>\n      <td>2218</td>\n      <td>0.358646</td>\n      <td>0.328424</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_calc(train, 'total', sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:20:51.036615Z",
     "start_time": "2024-03-28T12:20:51.027988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "       def_pd1  agr_pd1  def_cpd  agr_cpd  app_order_id  user_id       pd1  \\\ntotal                                                                        \nALL      842.0   2174.0    765.0   2174.0          2174      950  0.387305   \n\n            cpd  \ntotal            \nALL    0.351886  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n    <tr>\n      <th>total</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ALL</th>\n      <td>842.0</td>\n      <td>2174.0</td>\n      <td>765.0</td>\n      <td>2174.0</td>\n      <td>2174</td>\n      <td>950</td>\n      <td>0.387305</td>\n      <td>0.351886</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_calc(test, 'total', sum_col, count_col, unique_col, rate_tupes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 剔除高psi特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:20:52.812866Z",
     "start_time": "2024-03-28T12:20:52.792932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24210, 262) 107\n",
      "(10603, 262) 40\n"
     ]
    }
   ],
   "source": [
    "df1 = data_df[(data_df.loan_date<='2024-02-17')]\n",
    "print(df1.shape,df1['loan_date'].nunique())\n",
    "df2 = data_df[(data_df.loan_date>'2024-02-17')]\n",
    "print(df2.shape,df2['loan_date'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:20:56.183320Z",
     "start_time": "2024-03-28T12:20:55.639632Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 230/230 [00:00<00:00, 545.68it/s]\n",
      "100%|██████████| 230/230 [00:00<00:00, 2007.33it/s]\n"
     ]
    }
   ],
   "source": [
    "psi_rs1 = batch_calc_psi(df1,df2,model_features)\n",
    "psi_rs2 = batch_calc_psi(train,test,model_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:20:57.618333Z",
     "start_time": "2024-03-28T12:20:57.613221Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46, 2)\n",
      "(0, 2)\n"
     ]
    }
   ],
   "source": [
    "print(psi_rs1.query(\"psi>0.1\").shape)\n",
    "print(psi_rs2.query(\"psi>0.1\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:21:02.190202Z",
     "start_time": "2024-03-28T12:21:02.156046Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_rs = psi_rs1.merge(psi_rs2,on='var')\n",
    "feature_rs.to_excel('feature_base_info.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 启动特征挑选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:21:06.648089Z",
     "start_time": "2024-03-28T12:21:06.587144Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "171\n"
     ]
    }
   ],
   "source": [
    "fea_rs =pd.read_excel('feature_base_info.xlsx')\n",
    "model_cols_inited = fea_rs.query(\" psi_x<0.1 & psi_y<0.1 \")['var'].to_list()\n",
    "len(model_cols_inited)\n",
    "\n",
    "model_cols_inited_new = model_cols_inited.copy()\n",
    "for var in model_cols_inited:\n",
    "    if ('other' in var) :\n",
    "        model_cols_inited_new.remove(var)\n",
    "\n",
    "model_cols_inited = model_cols_inited_new.copy()\n",
    "print(len(model_cols_inited))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:21:14.437374Z",
     "start_time": "2024-03-28T12:21:12.891590Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 30 rounds\n",
      "Early stopping, best iteration is:\n",
      "[67]\tvalid_0's auc: 0.714681\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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7jStXrlCvXj3jnZIdO3bEuP2WLVsAKFCggHFdTOWlGzdujIuLC7NmzTKu0zSNOXPmkDVrVrMJqF/f/4MPPmDdunXRHunTp6ds2bKsW7eORo0aAfDLL79E265Hjx4ATJw4kWXLlhnP07x5cx4+fMjvv/9uXPfkyRNWr15No0aN3joWSIgE0XcaHyEsX2wThmqapq1evVoDtNmzZ2uapmnh4eFas2bNNECrXr26Nm3aNO2XX37RWrdurTk6OmpFihTRHjx4YHaMiIgIrVWrVhqglS5dWhszZow2f/58bcyYMVr58uU1QDt48GCy/KxCCCGSTmyfJ/Pnz9cArUaNGlpQUJCWMmVKrWjRotqgQYO0X3/9VZs2bZrWqFEjDdDKlSunhYWFGfdt06aNBmjXr183O2a/fv00QOvcubM2d+5crWHDhhqgLVu2zGy72PZ/3dsmDH3bzxgeHq5VrFhRS5UqlTZy5Eht5syZWpEiRbTUqVNrFy9efOtxhUiImCf7EELESdOmTcmTJw8TJ06kU6dOODk5sWrVKhYvXsyvv/7K0KFDCQ0NJU+ePAwfPpzvvvuOlClTmh3D0dGRxYsX07hxY3755RcmTpyIv78/6dOnp3r16vz0009UqlRJp59QCCFEUmvXrh3Pnj2jb9++fPrpp8yZM4fNmzezatUq7t27h6Zp5M6dm++//54BAwbEOldbVOPGjSNNmjT8/PPPLFy4kHz58rF06VJatmyZDD9RdE5OTmzZsoV+/frxv//9j6CgIMqVK8fChQvN7lAJkZgcNC2GvjJCCCGEEEIIYcVkjI4QQgghhBDC5kiiI4QQQgghhLA5kugIIYQQQgghbI4kOkIIIYQQQgibI4mOEEIIIYQQwuZYRXlpg8HAvXv3SJ06NQ4ODnqHI4QQdkPTNF6+fEmWLFlwdJRrY5Hkc0kIIfQT188mq0h07t27h4+Pj95hCCGE3bp9+zbZsmXTOwyLIZ9LQgihv7d9NllFopM6dWpA/TCenp46RyOEEPbD398fHx8f4/uwUORzSQgh9BPXz6Z4Jzp79+5lwoQJnDhxgvv377Nu3TqaNGnyxn12795Nnz59OHfuHD4+PgwZMoS2bdvG+ZyR3QI8PT3lA0UIIXQg3bPMyeeSEELo722fTfHucP3q1StKlCjBzJkz47T99evXadiwIe+//z6nT5+md+/edOzYkb/++iu+pxZCCCGEEEKIOIn3HZ369etTv379OG8/Z84ccuXKxaRJkwAoVKgQ+/fvZ8qUKdStWze+pxdCCCGEEEKIt0ryMTqHDh2iVq1aZuvq1q1L7969Y90nJCSEkJAQ47K/v39ShSeEEInu1SsIDn7zNgcOgK9v0sXw8iVMmAA5c755OwfNgJsWTLCjB/PnQ6VKSReTEEIIkZySPNF58OABGTNmNFuXMWNG/P39CQoKIkWKFNH2GTt2LCNHjozXeQwGA6Ghoe8Uq7BOLi4uODk56R2GsFPXr0PPnhBZ3XL7dggM1DemqC5ejP01d4JYylekIoCP2ERgoEvyBWYnIiIiCAsL0zsMkYjkM0cI62GRVdcGDRpEnz59jMuRlRViExoayvXr1zEYDMkRnrBA3t7eZMqUSQZMiwR79AiePn3zNteuwbZtcPs2/P47pEgBQUHvdt7WrSGp/mzDwqBoUahSJfprLi8eU3hwY7zOHcLg4srRaSfIWbpi0gRihzRN48GDB7x48ULvUEQSkM8cIaxDkic6mTJl4uHDh2brHj58iKenZ4x3cwDc3Nxwc3OL0/E1TeP+/fs4OTnh4+MjE9rZGU3TCAwM5NGjRwBkzpxZ54iEJdA09YjJjh1w44Zp+cEDGDYsYeeJmuSULAnduql2ihTQqBGkSvXm/R0cki7JeaMrV6BDffWcJg2O69dTqrokOYkpMsnJkCEDHh4e8oXYRshnjhDWJckTnUqVKrFlyxazddu2baNSInUEDw8PJzAwkCxZsuDh4ZEoxxTWJTJhfvToERkyZJAuBXbu1i3IkSPh+7/33ptff/YMGjdWY19q14Y8eSB3bnB1Tfg5k9WhQ/Dxx/Dkifoh/vwTChbUOyqbEhERYUxy0qZNq3c4IpHJZ44Q1iPeiU5AQABXrlwxLl+/fp3Tp0/z3nvvkT17dgYNGsTdu3dZvHgxAF26dGHGjBn079+f9u3bs3PnTlatWsXmzZsT5QeIiIgAwNVqvmWIpBCZ5IaFhcmHjh1Zvx7OnTPdFfnf/+C1G8ix+vhjUzswEKpVg3791N0Ym7VpE3z6qaqUULasWn5tDKU10mN+tzeJHJMjF99sl3zmCGEd4p3oHD9+nPfff9+4HDmWpk2bNixcuJD79+9z69Yt4+u5cuVi8+bNfPvtt0ybNo1s2bLx66+/JnppaekWYN/k928fLl2Cw4dhxgw4duzN27Zrp6qOvc7REdKkSZr4LJ6PD7i4QK1asGIFpEypd0SJInJ+t/bt29O0adO3bh85v1uXLl1YtmwZO3bsoGPHjmTOnDlRP5vkfcl2ye9WCOsQ70SnZs2aaLF1fgcWLlwY4z6nTp2K76mEEILwcPjwQ9i/H2KrN9Khg+mujpMTjBxpEzcqEl+JEnDwoOqq5myRtWgSROZ3E0IIERPb+aQTQtiM0FBVTGDXLojp+2vFiurmxNdfwwcf6DSg3xoEBkLHjqpKQmTptaJF9Y3JAsj8bkKIxBIWEcbDVw8JDAvktt9tHgc+jtN+QWFBnH98ntCIUE49OIVBs/3Kwc7hBjI8DyXTk2AyPwkhXdZ8dBq95e07vss5k/To4q0OHTpE1apVqVevntm4pd27d/P+++/z/PlzvL29zfbJmTMnvXv3NvtQ3rVrFxMmTODIkSMEBQWRM2dO6tevT58+fciaNWuCYps5cyYTJkzgwYMHlChRgunTp1O+fPlYt69ZsyZ79uyJtr5BgwbGny0gIICBAweyfv16nj59Sq5cuejZsyddunR543G+/vpr5syZk6CfQ1imu3fN53gJD4fZs2HDhpi3d3ODf/9VhQZcZLqXt3v8WJV+O3IE9uyBq1fB3V3vqCxCcs3vJuInps82IfQQYYggIDSAkIgQLj65yKvQVxg0A3tv7mXthbX4hfgBYNAMPA96jkbsPZ2slYMB3CLALTxuz2kDIXMAZH4JGV9BmiDwCgGvYPAMUe0U4ebnOFnwCYxO2p9DEh2dzZs3jx49ejBv3jzu3btHlixZ4n2Mn3/+mW+++YY2bdqwdu1acubMya1bt1i8eDGTJk1i8uTJ8T7mypUr6dOnD3PmzKFChQpMnTqVunXr4uvrS4YMGWLc5/fffzebtPXp06eUKFGCTz/91LiuT58+7Ny5k6VLl5IzZ07+/vtvvvnmG7JkycLHUUaHd+rUiR9++MG4LIN6rVdgoJqfZtAg2LpVDQt59ertc9ZE1bEjjBsHUsAqji5dggYNVHKTJo0ajyNJzjuJ7/xuwjK1bduWFy9esH79er1DEToyaAbCIsL46+pfLDi9gLMPz6rS4WGBvAx9SWBY/GZ9dnZ0xt3Znayps5I5dWYceHs3A0cHRwqlK0RK15Q44ECGlBnI4f0OJUMjjxsaRqob90h78iJevjdwCgnFMTQMx9BwnELDjG3HMNV2ilwOjVz+77XwiHeOJSYRri4EZk3Pq6wZSVu6eJKcIypJdHQUEBDAypUrOX78OA8ePGDhwoUMHjw4Xse4c+cOPXv2pGfPnkyZMsW4PmfOnFSvXj3Bk9VNnjyZTp060a5dO0D1ad+8eTPz589n4MCBMe7z3mt1eVesWIGHh4dZonPw4EHatGlDzZo1AejcuTM///wzR48eNUt0PDw8yJQpU4JiF/q6eBEih+T9+KOqihbV6wlOsWKmdni4mnvmm29UCWdnZ0idOmnjtTkHD6qSck+fQq5csGWLlI9+TVLP7yaSX1hYGC5yq1dEERIewv5b+9l6ZSs3/G5w7O4x0qRIg6ZpXH9xHf+QuHU/zZQqE1lTq54xrk6uFEhXgNbFW5MxlbornM4jHek80uHooOM8jhcvwt9/qyuCjx5BRBIkKW5ub36kSQOZM0OmTOrx3nvg5aUenp7GtpOXF6kdHUmuj3abS3Q0TV1B1oOHR/zGCqxatYqCBQtSoEABvvrqK3r37s2gQYPiVc1l9erVhIaG0r9//xhfj+z2duvWLQoXLvzGYw0ePJjBgwcTGhrKiRMnGDRokPE1R0dHatWqxaFDh+Ic27x58/j8889JGaWyU+XKldm4cSPt27cnS5Ys7N69m0uXLpklaQDLli1j6dKlZMqUiUaNGjF06FC5q2MFNmyA2Kr6urlBSAhs367e7wAKF1b/b0QiWbsWvvxS/UPbUPnoxJbU87vFJPJqsR48XOI3YenLly/p0qUL69evx9PTk/79+7NhwwZKlizJ1KlTef78Ob169eKPP/4gJCSEGjVq8L///Y98+fIZj7F27VqGDRvGlStXyJw5Mz169OC7774zvv7o0SM6dOjA9u3byZQpE6NHx6//ioODA7NmzeLPP/9kx44d9OvXj6FDh9K5c2d27tzJgwcPyJ49O9988w29evUCYMSIESxatMi4P6hu3zVr1uT27dt89913/P333zg6OlKtWjWmTZtGzpw54xWX0I+maey/tZ/rL66z5vwa/rj0R7RtbvrdNFtO6ZISHy8fxn04joypMuLh4kFq19SkdktNatfUuDi54ICD5VbZCw+Hrl3h11/N13t5QalSULmyar8tSXnbw8XFagfD2lyiExj49tnIk0pAQPyqtc6bN4+vvvoKgHr16uHn58eePXuMdzvi4vLly3h6er51duYsWbJw+vTpN24TeUfmyZMnRERExNiH/WLUQRVvcPToUf7991/mzZtntn769Ol07tyZbNmy4ezsjKOjI3PnzqV69erGbVq2bEmOHDnIkiULZ8+eZcCAAfj6+vL777/H6dxCHxcumCc577+vSjmHhKjv37H0eBSJadUq9Q/eqBH89pvNlI9+G0ub3y0mgWGBpBqrz4dTwKAAUrrG/W+hT58+HDhwgI0bN5IxY0aGDRvGyZMnKVmyJKC6f12+fJmNGzfi6enJgAEDaNCgAefPn8fFxYUTJ07w2WefMWLECFq0aMHBgwf55ptvSJs2rXGuorZt23Lv3j127dqFi4sLPXv25NGjR/H6uUaMGMG4ceOYOnUqzs7OGAwGsmXLxurVq0mbNi0HDx6kc+fOZM6cmc8++4y+ffty4cIF/P39WbBgAaA+98LCwqhbty6VKlVi3759ODs7M3r0aOrVq8fZs2dlnj4LZtAM/PbPb8w+PpuLTy7yNCh6n+gymcvwUf6PcMCBbJ7Z8PHyIY17GvK+lxdPN0+cHK1oHqSICFi9GjZvVoNWz59X1XsAypRRXSS++kqq9ERhc4mOtfD19eXo0aOsW7cOAGdnZ1q0aMG8efPilehomhanKw3Ozs7kzZs3oeHG27x58yhWrFi04gXTp0/n8OHDbNy4kRw5crB37166detGlixZjFWQOnfubNy+WLFiZM6cmQ8//JCrV6+SJ0+eZPsZRNy8eAFDh6q5bSJt2gQNG+oWkv1auBAqVIBevVSdbTthqfO7WaOXL1+yaNEili9fzocffgjAggULjONHIxOcAwcOULlyZUDdgffx8WH9+vV8+umnTJ48mQ8//JChQ4cCkD9/fs6fP8+ECRNo27Ytly5d4s8//+To0aOUK1cOUJ8ZhQoVilesLVu2NHavjhS1YESuXLk4dOgQq1at4rPPPiNVqlSkSJGCkJAQs67RS5cuxWAw8Ouvvxo/TxcsWIC3tze7d++mTp068YpLJL1wQzhbr2xl+O7hnLx/0uy1itkqkt4jPbVz16Z9qfbxSvItXvfu8HphJk9PmD8fmjXTJyYLZ3OJjoeHurOi17njat68eYSHh5sVH9A0DTc3N2bMmIGnpycAfn5+0aquvXjxAq//+v7kz58fPz8/7t+//8a7OvHpupYuXTqcnJxi7MMel3Ezr169YsWKFWbFBACCgoIYPHgw69ato+F/34KLFy/O6dOnmThxYrRyr5EqVKgAwJUrVyTR0VFoKNy5Y1r284PSpaNvN3SoJDnJJjBQdVno0UNdvUuRAqIMmLcX1jC/m4eLBwGD9Plw8nCJ+4fTtWvXCAsLM7tI5eXlRYECBQC4cOECzs7OxvdlgLRp01KgQAEuXLhg3KZx48Zmx61SpQpTp04lIiLCeIwyZcoYXy9YsGC0z7q3KVu2bLR1M2fOZP78+dy6dYugoCBCQ0ONd6Jic+bMGa5cuULq1wYEBgcHc/Xq1XjFJJLW9efXGblnJOsurjOOsUnhnIKsnlkpmakkP3/0M++leO8tR7FCvr6qos9/F8fp3Fl90ObPD7lzg9x1jJXNJToODpbfWyM8PNxYEe31K0VNmjTht99+48svv8TR0ZETJ06QI4epCse1a9fw8/Mjf/78ADRv3pyBAwfy008/RRvnAiop8vb2jlfXNVdXV8qUKcOOHTto8l9fJIPBwI4dO+jevftbf77Vq1cTEhJi7JYXKSwsjLCwMBwdzQfsOTk5YYhtJkgwxv227nkiaZw7B/37qzHtb+LiAuvXq2JfIhk8eqSKDhw5Ak+ewGsXFoRlcXBwsK0ryxYg5Wsf9itWrKBv375MmjSJSpUqkTp1auO0C28SEBBAmTJlWLZsWbTX0qdPn6gxi4QJiwhjzvE59Nza02x9zZw1mdNwDgXSFdApsmRw/DhUrw5BQWq5ZUv4+Wd9Y7IiNpfoWINNmzbx/PlzOnToYLwzE6lZs2bMmzePLl260LFjR7777jucnZ0pVqwYt2/fZsCAAVSsWNHYZcDHx4cpU6bQvXt3/P39ad26NTlz5uTOnTssXryYVKlSMWnSpHh3XevTpw9t2rShbNmylC9fnqlTp/Lq1SuzbgKtW7cma9asjB071mzfefPm0aRJE9K+VgvY09OTGjVq0K9fP1KkSEGOHDnYs2cPixcvNpbAvnr1KsuXL6dBgwakTZuWs2fP8u2331K9enWKF0/6MoRCFfQ4cwaCg1UBryjjhwF14Sjy4lFAANSooS4ypUmT/LHarUuX1Eyq166pyjbStUYkkty5c+Pi4sKxY8fInj07oHoWXLp0ierVq1OoUCHCw8M5cuSI8XPo6dOn+Pr6GnsNFCpUiAMHDpgd98CBA+TPnx8nJycKFixIeHg4J06cMHZd8/X1TXCV0KjnqFy5Mt98841x3et3ZFxdXYl4rSJV6dKlWblyJRkyZDD2phCWIzg8mBQ/mldD7Fq2K13KdqF4Rhv8XhASAjdvwpUraqzl0qVqfeHCsHKlTPocX5oV8PPz0wDNz88v2mtBQUHa+fPntaCgIB0iS5iPPvpIa9CgQYyvHTlyRAO0M2fOaEFBQdrw4cO1ggULailSpNBy5cqlde7cWXv8+HG0/bZt26bVrVtXS5Mmjebu7q4VLFhQ69u3r3bv3r0Exzl9+nQte/bsmqurq1a+fHnt8OHDZq/XqFFDa9Omjdm6ixcvaoD2999/x3jM+/fva23bttWyZMmiubu7awUKFNAmTZqkGQwGTdM07datW1r16tW19957T3Nzc9Py5s2r9evXL8bffVTW+HdgiY4c0TSV6kR/fPGFpl2+rHeEQtu/X9Pee0/9UnLl0rSLF5P0dG96/7Vntva5FFXHjh21XLlyaTt37tT+/fdfrVmzZlrq1Km13r17a5qmaY0bN9YKFy6s7du3Tzt9+rRWr149LW/evFpoaKimaZp24sQJzdHRUfvhhx80X19fbeHChVqKFCm0BQsWGM9Rr149rVSpUtrhw4e148ePa1WrVtVSpEihTZkyJU4xAtq6devM1k2bNk3z9PTUtm7dqvn6+mpDhgzRPD09tRIlShi3+fHHH7Xs2bNrFy9e1B4/fqyFhoZqr1690vLly6fVrFlT27t3r3bt2jVt165dWo8ePbTbt2/HeH5r/x1bg6eBT7UJByZojMD4KDG7hLb/5n69Q0saoaGa1qGDpjk6Rv8ArlhR006c0DtCixLXzyZJdIRNkL+D+Dt9WtM2blSPli01rXbt6O+tuXJpWooUmjZ/vt7RCk3TNG31ak1zc1O/nHLlNO3BgyQ/pSQ6MbPlzyV/f3+tZcuWmoeHh5YpUyZt8uTJWvny5bWBAwdqmqZpz54901q1aqV5eXlpKVKk0OrWratdunTJ7Bhr1qzRChcurLm4uGjZs2fXJkyYYPb6/fv3tYYNG2pubm5a9uzZtcWLF2s5cuR4p0QnODhYa9u2rebl5aV5e3trXbt21QYOHGiW6Dx69EirXbu2lipVKg3Qdu3aZYyndevWWrp06TQ3Nzctd+7cWqdOnWL9u7f237ElCw4L1qYcmqK5j3Y3S3JKzSmlhYSH6B1e4gsP17TlyzUta1bTh2/KlJpWrJimtWmjaceO6R2hRYrrZ5ODpr1hBKeF8Pf3x8vLCz8/v2i3lYODg7l+/Tq5cuXCXWb+tlvydxC7R4/g+XM1x03kZJ0//fTmffr1e/s2Ipndu6cGnYaEqLE5y5cny4DEN73/2jN7+lx69eoVWbNmZdKkSXTo0EHvcCyCrf2OLcX+W/uptqCa2bpSmUqx6tNV5H0v+SrHJpmAAFUe+sAB+Ocf8PdXXZAjS0SnSQP/+5+aD03KQ79RXD+bZIyOEDZI02D/fjWXzdsmSI4snvT4MQwYoMrvJ2MlchFXWbKoEqKHDsHUqXZVPlokr1OnTnHx4kXKly+Pn5+fsYLm65XUhEhMB28fNEtyupTpwugPRpPWI+0b9rIiL19CkSJw+3b019zcoF07mDxZVc8UiUYSHSFswPLlqiqai4u6YLRmTfRtvL3VnDeRxQWcnWH0aPUsLFRgIDx4oO7kgKq207KlvjEJuzBx4kR8fX2NVTj37dtHunTpkuXcy5Yt4+uvv47xtRw5cnDu3LlkiUMkn/OPz1N7SW3j8m/NfuPzop/rGFEie/pUTegZmeQMHqzmZ0iTRr2/+/jIxaskIl9xhLBST59Cp06msvqxqVtXbSMXiazMo0fQqBE8fKju4kh5dZFMSpUqxYkTJ3Q7/8cff2w2T09ULi4uyRyNSEoGzcA/D/+h5M8lASiesTgrm6+kYLqC+gaWWE6cgNmz1dwLkX3HFy+GVq10DcueSKIjhBXSNIjp4uqgQeDlBQYDVKkC1apJN1+r5Ourykdfv67KR9+5I4mOsBupU6eONnmnsD1/XfmLFmta4BfiZ1y39JOl1p3kGAzq/fv0aRgxQk0FEMnDAzZvhpo1dQrOPtlMomMFNRVEEnrThKO2QNPg8mU1XnHFCvjxR/PXIyfqlIudNmD/fmjcGJ49U10a/vxTzX4thBA24sS9E9RbVs+47IAD31b8lmIZi+kY1Ts4d04lNjH1G69TB7p0gapVQSagTXZWn+i4uLjg4ODA48ePSZ8+PQ5y+dquaJpGaGgojx8/xtHREdfImSxtyLhx6k5NbCTHtyGrV6suDSEhqkrExo2QIYPeUQkhRKIwaAa6burKLyd/Ma473OEwFbLF3FXRop05o7qlPXsGf/yhZtmOVKIEFCgAHTtC7dqxH0MkOatPdJycnMiWLRt37tzhxo0beocjdOLh4UH27NlxdHTUO5REdfRo9CQnfXr1vjp/vnTztSmrVkGLFqrduLGqMOHhoW9MQgiRCDRNY9W5VXTd3JXnwc+N69d+ttY6k5wlS6B1a/N1NWqoymnly0OhQvrEJaKx+kQHIFWqVOTLl4+wsDC9QxE6cHJywtnZ2ebu5vn7m0o/A2zfrko/29iPKSLVrg2FC8OHH8KUKVKBRwhhEy48vsBnaz7j30f/Gtd1K9eN/9X/H44OVnhx8uRJldCA6lb8zTeQK5cqHiMf0BbHJhIdUF92neSLgbABQUHQowfMm2da9+236vuvsDGhoRDZ3TJNGlVdLXVq+bAUQtiEOcfn0HVzV+Nyy2It+azwZ9TLW886kxyA779XE9SVKaPGVMqEsRbNSv/KhLBdRYuaJzlubjBmjH7xiCTy8KEanDp9ummdp6ckOUJXNWvWpHfv3ol6zN27d+Pg4MCLFy8S9bjCcvmH+NNkRROzJGf+x/NZ1nQZjQs2xs3ZTcfo3sGuXbB1q2rPmiVJjhWQREcICzJrFly7ZlreuVPd4ZH3Uhvj6wuVKsGxY2rWVj+/t+8jhHgjBwcH1q9fr3cYdu/MgzN4jfNig+8GQN3FCRwcSLtS7XSOLBFEjsvx8oKSJXUNRcSNzXRdE8LarVkD3bqZll+8UO+lwsbs26eKDTx/DnnyqPLR8osWIkYRERE4ODjYXKEZW3X+8XnK/1reuDz6/dF8X/17HSNKJAaD+oC+c0ctHzhg6nYsLJq8cwhhAUaPhk8/NS3Ld18btXIl1KqlkpwKFdSYnHz59I5KJAdNg1ev9HnEswZ9eHg43bt3x8vLi3Tp0jF06FDjXHVLliyhbNmypE6dmkyZMtGyZUsePXpktv+WLVvInz8/KVKk4P33349XRdSFCxfi7e3Nxo0bKVy4MG5ubty6dYtjx45Ru3Zt0qVLh5eXFzVq1ODkyZPG/XLmzAnAJ598goODg3EZYMOGDZQuXRp3d3dy587NyJEjCQ8Pj9e/iXi7vTf3UmRWEUIjQvF29+Zaz2u2keTcuwcVK8KcOWp51CgoUkTfmEScSaIjhM7mzYOhQ03LixZBvXqxby+s1KRJ8PnnqgDBJ5+ofokyeZz9CAyEVKn0eQQGxivURYsW4ezszNGjR5k2bRqTJ0/m119/BSAsLIxRo0Zx5swZ1q9fz40bN2jbtq1x39u3b9O0aVMaNWrE6dOn6dixIwMHDoznP1Ug48eP59dff+XcuXNkyJCBly9f0qZNG/bv38/hw4fJly8fDRo04OXLlwAcO3YMgAULFnD//n3j8r59+2jdujW9evXi/Pnz/PzzzyxcuJAfX591WbyT2363+XCxqWLOxNoTyZUml44RJZLnz6FmTdXN2M1Nlf0fMkTvqEQ8SNc1IZKJvz9895151eBnz9QckZGOHoVy5ZI/NpEMIrve9OwJkydL+WhhsXx8fJgyZQoODg4UKFCAf/75hylTptCpUyfat29v3C537tz873//o1y5cgQEBJAqVSpmz55Nnjx5mDRpEoBx//Hjx8f5/GFhYcyaNYsSJUoY133wwQdm2/zyyy94e3uzZ88ePvroI9L/d9HA29ubTJkyGbcbOXIkAwcOpE2bNsaYR40aRf/+/Rk+fHj8/3FENPNOzqPr5q6EG9RdsqWfLKVlsZY6R5UInj+HdOlUt7V06VQhgqJF9Y5KxJMkOkIkMU1TF/A3bHjzdnv3SpJj03r3VoNX339f70iEHjw8ICBAv3PHQ8WKFc3mJatUqRKTJk0iIiKC06dPM2LECM6cOcPz588xGAwA3Lp1i8KFC3PhwgUqVDCfALJSpUrxOr+rqyvFixc3W/fw4UOGDBnC7t27efToEREREQQGBnLr1q03HuvMmTMcOHDA7A5OREQEwcHBBAYG4iGT8iaYpmnUWVqH7de2A5DaNTXbWm2zzglAX6dp0LSpSnJAlZSWJMcqSaIjRBLLmBEePzYtZ84MXbuab1OvniQ5NufBAxgwAP73PzXgysFBkhx75uAAKVPqHcU7CQ4Opm7dutStW5dly5aRPn16bt26Rd26dQkNDU2086RIkSLaBNBt2rTh6dOnTJs2jRw5cuDm5kalSpXeet6AgABGjhxJ06ZNo73mLuUs38nck3ONSc6Xxb5kdsPZpHZLrXNUiWTDBti9W5U83bxZzdYtrJIkOkIkobZtzZOc27chWzbdwhHJ5eJFqF8fbtyA4GBVhEAIK3HkyBGz5cgxMRcvXuTp06eMGzcOHx8fAI4fP262baFChdi4cWO0/d/VgQMHmDVrFg0aNADUWKAnT56YbePi4kJERITZutKlS+Pr60vevHnfOQZhsvvGbr7e9DUAudPkZmnTpTpHlAiOH1fzO9y+DX37qnU9ekiSY+Uk0REiiZw6pQoLRHryBNKm1S8ekUz27oUmTVT/7rx5QQY9Cytz69Yt+vTpw9dff83JkyeZPn06kyZNInv27Li6ujJ9+nS6dOnCv//+y6hRo8z27dKlC5MmTaJfv3507NiREydOsHDhwneOKV++fMaKb/7+/vTr148UKVKYbZMzZ0527NhBlSpVcHNzI02aNAwbNoyPPvqI7Nmz07x5cxwdHTlz5gz//vsvo0ePfue47NHO6zvNCg9sablFx2gSwcuX0KlT9AtSpUqpCmvCqknVNSES2YsXqpdK6dKmdbt3S5JjF1asgNq1VZJTsSIcPKiSHSGsSOvWrQkKCqJ8+fJ069aNXr160blzZ9KnT8/ChQtZvXo1hQsXZty4cUycONFs3+zZs7N27VrWr19PiRIlmDNnDmPGjHnnmObNm8fz588pXbo0rVq1omfPnmTIkMFsm0mTJrFt2zZ8fHwoVaoUAHXr1mXTpk38/ffflCtXjooVKzJlyhRy5MjxzjHZox5bepglOUc6HqFAugI6RvSONA0KFDAlOfnzqyprDRrAggWq0pqwag6aFs8C+zrw9/fHy8sLPz8/PD099Q5HiFjduAG5Xquo2a0bTJkCLi66hCSSg6bBhAlqTA6oQaxLl8JrV5ytkbz/xuxN/y7BwcFcv36dXLlyyTgQG2WPv+O159fSfHVzAMplKceO1juse0xOeLgqFR1ZEXDWrOgDaIXFiutnk3RdE+IdGAywZw8MHw6+vhB13jxnZwgJMVUVFjbsxQuYPl21e/eGiROlfLQQwqZ0+qOTsW31Sc6WLdC5M9y9q5Zr1pQkx0bJVzAhEkjToFIlNU5x3z7zJOf991XRLUly7ESaNOqDc/p0dftOkhwhYlS/fn1SpUoV4yMxuriJpDH/1HyeBz8H4FinY9ab5ISFQfPm0LChKcmpXRu2btU3LpFk5I6OEAnw779QvboaihHp/fehfXv46CPw9tYtNJFcHjxQFSfq11fLxYqphxAiVr/++itBQUExvvbee+8lczQiLqYensq3f30LQIsiLSibpazOEb2DAQNg7VrVbt4c5s0D6ZJr0yTRESKOzp9XXdTWrIn+2t27kCVL8sckdHLhgkpw7t+H7duhWjW9IxLCKmTNmlXvEEQ8dNvcjVnHZwFQNXtV6y0jHRYGrVqZig58/TXMmaNvTCJZSMcaIeIgLAyKFIme5LRpo4ZnSJJjR/bsgcqV4eZNyJFDzQArhBA25kHAA2OSA7CoySKcHa30+viPP6okx9ERvvxSyv7bESv9ixUiefz7r6oQ/PXXpnW1akHLlvDZZ1Y/0bmIr+XLoV07CA1VA7Q2boR06fSOSgghEtXLkJeU+aUMAA44EDEsAgcHB52jSqCgIJg0SbWXLFEf4MJuSKIjxH8CA9VkyPPmgasrBATEvN22bckbl7AAmqZKkA4apJabNVMfmDZQPloIIaK68uwK1RdU537AfQA2tdxkvUkOqLs3AQGQKRN8/rne0YhkJomOsHuaBsuWqe67kUJDzbcpX171Vvrpp+SNTViI9etNSU6fPmrOHCmpJ4SwMZqm0WZ9G2OSc6TjEcpnLa9zVO+gZUv47TfV/vFHed+2Q5LoCLumafDee2qcTaSMGdVYnMhxNzlySLVgu9e4sfrArFABevbUOxohhEgSn67+lIO3DwIwp+Ec605y/vrLlOR88gm0batrOEIfktoKu3XunEpqoiY5CxeqqsFVq0Lu3OohSY6devgQgoNV29ERli6VJEeIJJQzZ06mTp0a6+s3btzAwcGB06dPJ/q527ZtS5MmTRL9uNZkyZklrL2gSi9Xy16NNiXb6BzRO/r5Z/WcMyesWiV3c+yU/NaFXerZE4oWhceP1XKGDBARoaqoCcH586q/Ytu2YDCoddbcR10IId7g5oubdN7UGYCmhZqyt91e3J3ddY7qHaxaBevWqfa6deAsHZjslfzmhV0JCQH31967W7eGmTPlYo/4z+7d0KQJ+PmBmxs8eaIyYSGEsEEXn1yk0MxCAJTKVIpFTRbpHFEiiKyyVqYMlCihbyxCV/LVTtiNFy+iJzk3bsCiRZAqlR4RCYuzfDnUqaOSnCpV4NAhSXJEotA0ePVKn4emxS/Wly9f8uWXX5IyZUoyZ87MlClTqFmzJr179wbg+fPntG7dmjRp0uDh4UH9+vW5fPmy2THWrl1LkSJFcHNzI2fOnEyK/OL5n0ePHtGoUSNSpEhBrly5WLZsWZzju3jxIpUrV8bd3Z2iRYuyZ88e42sRERF06NCBXLlykSJFCgoUKMC0adPM9o+IiKBPnz54e3uTNm1a+vfvjxbffyQb8TzoOR8s+sC4vOSTJaRytfIPxEmT4OhR1d64Ue7G2zlJdIRd6NYN0qQxXxcergoNCIGmwdixaiK5sDD49FPYvh3SptU7MmEjAgPVBRU9HoGB8Yu1T58+HDhwgI0bN7Jt2zb27dvHyZMnja+3bduW48ePs3HjRg4dOoSmaTRo0ICwsDAATpw4wWeffcbnn3/OP//8w4gRIxg6dCgLFy40O8bt27fZtWsXa9asYdasWTx69ChO8fXr14/vvvuOU6dOUalSJRo1asTTp08BMBgMZMuWjdWrV3P+/HmGDRvG4MGDWbVqlXH/SZMmsXDhQubPn8/+/ft59uwZ6yK7OdmR4PBgvvz9S+4H3CeVayp2tN5BkQxF9A7r3Vy9CgMHqnalSjKbtwDNCvj5+WmA5ufnp3cowsoYDJqmvsWaHgULqvVCGPXrZ/oD6dtX0yIi9I7IYsj7b8ze9O8SFBSknT9/XgsKCjKuCwiI/l6UXI+AgLj/XP7+/pqLi4u2evVq47oXL15oHh4eWq9evbRLly5pgHbgwAHj60+ePNFSpEihrVq1StM0TWvZsqVWu3Zts+P269dPK1y4sKZpmubr66sB2tGjR42vX7hwQQO0KVOmxBrb9evXNUAbN26ccV1YWJiWLVs2bfz48bHu161bN61Zs2bG5cyZM2s//fRTtGM0btw41mO8LqbfsbXpuqmrxgg0RqAtOLVA73ASx5Il6o8+VSpNe/xY72hEEorrZ1OC7ujMnDmTnDlz4u7uToUKFTgaeYswFlOnTqVAgQKkSJECHx8fvv32W4IjqxkJkUSuXo0+7ubmTbhwQe5ki9c0aqQm/5w+XebIEUnCw0PNWajHw8Mj7nFeu3aNsLAwypc3lRX28vKiQIECAFy4cAFnZ2cqVKhgfD1t2rQUKFCACxcuGLepUqWK2XGrVKnC5cuXiYiIMB6jTJkyxtcLFiyIt7e3cblLly6kSpXK+IiqUqVKxrazszNly5Y1nhvUd5QyZcqQPn16UqVKxS+//MKtW7cA8PPz4/79+2bxRx7DnozYPYLZx2cD0L1cd9qUsIFKPM+fw/ffq/bnn0O6dPrGIyxCvIsRrFy5kj59+jBnzhwqVKjA1KlTqVu3Lr6+vmSIoS/78uXLGThwIPPnz6dy5cpcunSJtm3b4uDgwOTJkxPlhxAiJt98Y74cGgouLvrEIiyQwWBKaKpVg2vX1MzZQiQBBwdImVLvKKzHDz/8QN++feO934oVK+jbty+TJk2iUqVKpE6dmgkTJnDkyJEkiNI6PQx4yKi9owB4P+f7/K/+/3Cw9qt/9+6pMtL/dZ+kUyddwxGWI96XLSdPnkynTp1o164dhQsXZs6cOXh4eDB//vwYtz948CBVqlShZcuW5MyZkzp16vDFF1+88S5QSEgI/v7+Zg8h4iI4GJ4+VUUG/v5brcuSRX2nlSRHGJ07ByVLwj//mNZJkiMEuXPnxsXFhWPHjhnX+fn5cenSJQAKFSpEeHi4WeLw9OlTfH19KVy4sHGbAwcOmB33wIED5M+fHycnJwoWLEh4eDgnTpwwvu7r68uLKJOaZciQgbx58xofUR0+fNjYjjxOoUKFjOepXLky33zzDaVKlSJv3rxcvXrVuL2XlxeZM2c2i//1WGxZcHgwdZbWwaAZyJ82Pzvb7LT+JEfTVPnUyCRn2DA1PYAQxDPRCQ0N5cSJE9SqVct0AEdHatWqxaFDh2Lcp3Llypw4ccKY2Fy7do0tW7bQoEGDWM8zduxYvLy8jA8fH5/4hCnsUFgYfPyx6n2ULh3kymV6bf166aomoti5U1VU++cf+PZbvaMRwqKkTp2aNm3a0K9fP3bt2sW5c+fo0KEDjo6OODg4kC9fPho3bkynTp3Yv38/Z86c4auvviJr1qw0btwYgO+++44dO3YwatQoLl26xKJFi5gxY4bxDk2BAgWoV68eX3/9NUeOHOHEiRN07NiRFClSxCnGmTNnsm7dOi5evEi3bt14/vw57du3ByBfvnwcP36cv/76i0uXLjF06FCzpA2gV69ejBs3jvXr13Px4kW++eYbsyTLln296WvOPjwLwNgPx+ocTSL5+2/YsUO1f/wRRo7UNx5hWeIz8Ofu3bsaoB08eNBsfb9+/bTy5cvHut+0adM0FxcXzdnZWQO0Ll26vPE8wcHBmp+fn/Fx+/ZtGQwrorlyRdNy59Y0F5fYB+E2aaJ3lMKiLFli+oOpWlXTnjzROyKLZ03FCGbMmKHlyJFDc3Nz08qXL68dOXLkjdtPmTJFy58/v+bu7q5ly5ZN6927d5wHl8e3GIE18ff311q2bKl5eHhomTJl0iZPnqyVL19eGzhwoKZpmvbs2TOtVatWmpeXl5YiRQqtbt262qVLl8yOsWbNGq1w4cKai4uLlj17dm3ChAlmr9+/f19r2LCh5ubmpmXPnl1bvHixliNHjjgVI1i+fLlWvnx5zdXVVStcuLC2c+dO4zbBwcFa27ZtNS8vL83b21vr2rWrNnDgQK1EiRLGbcLCwrRevXppnp6emre3t9anTx+tdevWNl2M4LbfbWPhAUagdd3UVe+QEk/z5uo9vX17vSMRySiun01Jnujs2rVLy5gxozZ37lzt7Nmz2u+//675+PhoP/zwQ5zPa00ftCJ5nDkTe3Jz86YqmiWFs4SRwaBpo0eb/kg++0zTrOQLit6s5f13xYoVmqurqzZ//nzt3LlzWqdOnTRvb2/t4cOHMW6/bNkyzc3NTVu2bJl2/fp17a+//tIyZ86sffvtt3E6ny0nOq8LCAjQvLy8tF9//VXvUCyGNf2OwyLCzJKctuvb6h1S4tm40fS+vn273tGIZJQkVdfSpUuHk5MTDx8+NFv/8OFDMsXSv33o0KG0atWKjh07UqxYMT755BPGjBnD2LFjMRgM8bv9JOzaixewZ4/qhhZ1ouMKFeDIEbhzR73bZc+uxphL4SwBqH6NnTvDkCFquW9f+O236LPHCquWHONH7cWpU6f47bffuHr1KidPnuTLL78EMHZNE9bj9IPTuIwyDVBd0HgBCxov0DGiRHT+vOqzDlCwIHzwwZu3F3YpXl8FXV1dKVOmDDsi+0KiJufasWOHWbnHqAIDA3F87Runk5MTgN3ORCziJiICFi2CQYNU0pImDdSsab5N375w+LAad5g1qy5hCkunaXD9uvojmjFDykfboOQYP2pvRXImTpxIiRIlqFWrFq9evWLfvn2kk3K9VuXGixuU+rmUcXlw1cG0LdlWv4AS04sX6ipnpBkzZDCuiFG8y0v36dOHNm3aULZsWcqXL8/UqVN59eoV7dq1A6B169ZkzZqVsWPVILdGjRoxefJkSpUqRYUKFbhy5QpDhw6lUaNGxoRHiJgsXAgdO0ZfnyOHKjiwaxekTp3sYQlr4+oKa9eq23516ugdjUgCT548ISIigowZM5qtz5gxIxcvXoxxn5YtW/LkyROqVq2KpmmEh4fTpUsXBg8eHOP2Y8eOZaSdDHIuVaqU3VQhs1Uh4SHkmmaqyrOt1TZq5a71hj2siKZB5cpqkiiA5cvhww/1jUlYrHgnOi1atODx48cMGzaMBw8eULJkSbZu3Wr8gLl165bZHZwhQ4bg4ODAkCFDuHv3LunTp6dRo0b8+OOPifdTCJuhaXDliprsM2qS06YNFC8ObdvCe+/pFp6wFv/+C+vWqe5qDg7g5SVJjjCze/duxowZw6xZs4wX4Xr16sWoUaMYOnRotO0HDRpEnz59jMv+/v5SEVRYpMCwQNL9ZLr7Nu/jebaT5AAsWaJm/gaYPBm++ELfeIRFi3eiA9C9e3e6d+8e42u7d+82P4GzM8OHD2f48OEJOZWwI5oWc4+i335TkxwLESc7d8Inn4C/v5pEqUMHvSMSSexdx48CFCtWjFevXtG5c2e+//77aF2u3dzccHNzi1dc0j3bdlny77b7lu4EhQcBUNmnsu10V4u0erV6zp9fjb8U4g2ko7qwCOHhEFPX+K5doUWL5I9HWKklS6BePZXkVKumEh5h8yxt/KjLf7MTBwYGvtNxhOWK/N26WNhM1PNPzWfBaVVsYFj1YRxofwBHBxv6qnfpEmzapNq9ekHKlPrGIyxegu7oCJHYXF3VHZ1IBoOMKxTxoGlqorjILkeffw4LFkhlNTtiSeNHnZyc8Pb25tGjRwB4eHhY/+zzAlBJcGBgII8ePcLb29uixhpfeHyBDhvVHey2Jdsy8n0bG1MWEWG6Ipo1K/w3SawQbyKJjtDd11+bJznHjkmSI+IhLEzd+ps3Ty0PGABjxkhlNTtjaeNHI7vMRSY7wrZ4e3vH2i1SLyV/LglAGvc0TK8/Xd9gEpvBoMqsXr0KHh6webNcyBJx4qBZckfT//j7++Pl5YWfnx+enp56hyMSicEAPXvCzJmmdaGhYGE9AYSl27cPatRQ2fGMGSrpEYlG3n9jFtd/l4iICMLCwpIxMpHUXFxcLOpODsCKf1fwxVo1KH9P2z1Uz1Fd54gS2Y8/muZCGzsWBg7UNx6hu7i+B8sdHZHsNE1Vg/zqK/P1ly5JkiMSoFo1lS1nywaNGukdjRBmnJycLO5LsbAtIeEhxiTn08Kf2l6SA7B/v3pOm1bdtRcijiTREcnq1Sto2hT+/tt8/dWrkDu3PjEJK/TPP+DpqSZVArmLI4SwWyv+XWFsT6ozScdIksj27fDXX6o9ebL0bRfxIp3YRZLaulW9J2XJoh6pUpknOdOmqTs8kuSIONuxA6pWhfr14flzvaMRQghdtd3QFoBu5brh42WDczvNn6++KDRpEr0riBBvIYmOSDKdOqnvogD376tHpCxZ4NQpNUZHiDhbtMhUPjp9evMqFkIIYWc6bjTNrN2yWEsdI0kiQUFqMj2AHj2kyIyIN+m6JpLE48fw66+m5dGj4aOPVDtzZsiQQZ+4hJXSNBg1CiInHv78c1i4EOI5gaMQQtiKvTf3Mu+UqjZZKVslKvtU1jmiJLBunaldoYJ+cQirJYmOSBLFipnaDx7AfxVehYi/sDBVg3yBmgRPykcLIQSM2z8OgGye2TjQ/oDO0SSRDRvUc5MmMjmoSBD5piASjcEAe/eqiy4PH6p1+fJJkiPeUf/+KslxdITZs2HcOElyhBB27fzj8/x55U9AFSCwuQlp792DkiVh1Sq1LJODigSSOzoi0dSuDTt3mq87c0afWIQN6d9fVbAYP97U/1EIIezYotOLAGhcoDGfFflM52gSWXAwVKkCN26o5T595L1fJJgkOiJRrFljnuRUq6aGUKRIoVtIwpo9farmSwA1qOvsWZC5SIQQgmdBz/jp4E8AfFH0C52jSQKzZ6skJ0UK1W7TRu+IhBWT/h/inWiaKnH/6aemdXfuqC5sUjJaJMj27ZAnj5pVNpIkOUIIAcCCUwuM7U8KfaJjJEnAz0/dwQGYNEmSHPHOJNERCWYwqOlMatc2rVu6FLJm1S8mYeUWLlQ1yf38VFvKRwshhJFBMzDtyDQAfvzgR1ydXHWOKJEtW2ZqR72CKkQCSaIjEmzuXDh40LQ8eTJ8+aV+8QgrpmkwciS0awfh4fDFF/DHHzIDthBCRNFxY0du+9/G082THuV76B1O4ov8UlGsGKRLp28swibIGB2RYEePmtp376pJQIWIt7Aw6NxZ3cEBGDRITbwkldWEEMJo65WtLDituq0VzVCU1G6pdY4okYWFwcaNqj1hgr6xCJshiY5IsPnz1XOnTpLkiAQKC4OGDWHbNpXYzJql5swRQghhZurhqcb2nrZ79AskKTx4ANmyQUQEeHpCxYp6RyRshFwyFQny/LmpXaOGfnEIK+fiAuXKqYng/vhDkhwhhIiBX7Aff139C4D1Ldbj7Ghj16mbNVNJDsAvv4CXl77xCJshiY5IkJAQU7tlS/3iEDZg9Gg14VKDBnpHIoQQFuefh/+QcaKaeTtL6iw0yGdj75XBwaaxOTVqQIsW+sYjbIokOiJBvv9e7wiE1dq2TVVWCwpSyw4Oqpy0EEIIM75PfCk7tywhEerq4i8f/YKLk4vOUSWy9etN7XXrdAtD2CZJdES83bplGp8DUhhLxMOCBerOzdatMHGi3tEIIYRF67utL6ERoaT3SM/ZLmdpmL+h3iElvp/U5Ke0bw9p0ugbi7A5kuiIeNE0yJHDtDx1qm6hCGuiaTBihPogCw9X/R3799c7KiGEsFjPgp6x6dImAL6v9j3FMhbTOaIk8OgRnDql2iNG6BqKsE2S6Ig4e/HCvOJv6dJq2hMh3ig0VP2hjByplr//Xs0s6+amb1xCCGHBBu8YbGy3K2WDH7YGg7r4BZAvH/j46BuPsEk2VrZDJKW+fU1tZ2c4fly6rYm38PNT1XR27AAnJ1U+unNnvaMSQgiLduzuMX4+8TMAK5qtwNPNU+eIEpnBAD16wObNarl1a33jETZLEh0RJ1euwLx5puXQUElyRBw8fgynT6vy0atXqyIEQgghYqVpGrWW1AIgc6rMtChqg1XIpkxRF74AKleGfv30jUfYLEl0RJz88Yep/c8/kuSIOMqbV/3xuLmpvo5CCCFiFWGIoPic4viH+AOwvNlynSNKApoG48erduPGqtKafKkQSUQSHREnffqo50qVoGhRfWMRFu7vv9VznTrquVIl/WIRQggrsvr8as4/Pg9A59KdqZHDBmfk3rtX3e0H+PlnSXJEkpJiBOKtIufxAsifX784hBWYP1+Vj27WDC5e1DsaIYSwKn9fVReKfDx9+LnRzzjYWhIQEQFNm6p2586QMaO+8QibJ4mOeKPVq6FKFdPywoW6hSIsmabBsGHQoYP6IGvSBHLn1jsqIYSwGo9ePWLN+TUATKozSedokoCmwddfw7Nnajmy4poQSUi6rolYPXwIn31mWu7SRb9YhAULDYWOHWHJErX8/fcwapR0RxBCiHhoubYlL0NfUipTKZoVbqZ3OInv119NVY06doTy5fWNR9gFuaMjYjRkCGTKZFqePx9mz9YvHmGh/PxUJbUlS1T56F9+gdGjJckRQoh4uPD4Ajuu7wCgR/keODrY2NczPz/T1AKtW8PcufI5IZKF3NERZoYNUxUfnz41rWvfXiYGFbGYOhV27oRUqWDVKikfLYQQCbDRd6Ox3bZkW/0CSSre3qZ2//66hSHsjyQ6AoDnz+G996Kvv3pVhlqINxg8GK5dg969oVQpvaMRQgirNP6AKrc8rPow2ytAcPy4qd29OxQpol8swu7Y2L1RkVAffWS+vHy5qv4oSY6I5uhRCA9XbRcXWLRIkhwhhEigZWeX8Tz4OQBdytrgYNgVK0zt6dP1i0PYJUl0BAaDqYR0unSqMMoXX6i2EGbmzVOzWPfsqf5QhBBCJNjSs0v5at1XAJTKVIrMqTPrHFEii4iASf9VkPvtN31jEXZJEh3BL7+Y2ocP6xeHsGCaBkOHqko5EREQEKAyZCGEEAky7+Q8Wq1rZVz+66u/dIwmiQwYYGp//LF+cQi7JYmOndu7F7p2NS3nyaNfLMJChYZCmzaqmhqohGfRIlVlTQghRLy9DHnJkF1DAMicKjOP+j4ifcr0OkeVyKLezUmbFjw89I1H2CUpRmDHgoKgRg3T8po1+sUiLNSLF2oW6127TOWjZZI3IYR4J39c+oMHAQ8AONv1LOk8bLCveOPGpvaFC/rFIeyaJDp2bMoUU3vFCmhmg/OTiXdgMEDduqr4QKpUKhOuW1fvqIQQwuptvrwZgK/LfG2bSc7Tp7BZ/YwULAjpbexulbAa0nXNjkUdF9iihX5xCAvl6KhmjvXxgX37JMkRQohEEBwezB++fwDQqnirt2xthTZtgvLlTcv//qtfLMLuSaJjR54+hWXLoGFDNSFx5HvP2LH6xiUszKtXpnajRnDpEpQsqVs4QghhS37Y8wMvQ1+SIWUGKvlU0jucxDVihPrcuHZNLS9dKuM5ha4k0bEDmgbTpqly0V99BVu2mL/esqU+cQkLNHcu5M8P16+b1rm76xePEELYkN03djN2v7q6+F2l73B0sKGvYRERMHKkaXnzZvjyS/3iEQJJdOxCuXJq4vpIWbKo8eXz5qmCBNmz6xaasBSaBt9/D507w717MH++3hEJIYTNmXp4KgCN8jeiX+V++gaT2MaNM7X9/aFBA/1iEeI/UozAxg0cCCdOmJb/+gvq1NEvHmGBQkKgQwfVrxFg2DDV/UAIIUSiOXHvBBt8NwDwU+2fcHBw0DmiRLRkiRrTCdC6NaROrW88QvxHEh0bduECjB9vWg4IgJQp9YtHWKAXL+CTT2D3bnB2hp9/lvLRQgiRBBacXgBA1tRZKZiuoM7RJLKo3UbmzdMtDCFeJ4mOjXr5EgoXNi3/848kOeI1d++q23vnz6urb2vWyO0+IYRIImcengFgYNWBOkeSBFxd1fOiReqimRAWQv4abZSnp6ndsycULapfLMJCeXurmaqzZFEVKkqU0DsiIYSwSYFhgRy/dxyAWrlr6RxNIjMYVO8AgGrVdA1FiNclqBjBzJkzyZkzJ+7u7lSoUIGjR4++cfsXL17QrVs3MmfOjJubG/nz52fL66W/RKLx8zNfnjpVlzCEpUuZUs13cPiwJDlCCJGEtl7ZSnB4MDm8clAgbQG9w0lcBw9CcDCkSAHZsukdjRBm4p3orFy5kj59+jB8+HBOnjxJiRIlqFu3Lo8ePYpx+9DQUGrXrs2NGzdYs2YNvr6+zJ07l6xZs75z8CJmS5ea2gaDmjNHCAB++QV+/NG0nDGjmhBUCBsgF+GEpfp+5/cANC/c3LaKEIC6YAbw6afg4qJvLEK8Jt5d1yZPnkynTp1o164dAHPmzGHz5s3Mnz+fgQOj9zudP38+z5494+DBg7j89x8gZ86c7xa1eKNTp0xtW3s/FQlkMKiKOJGzw1avLl0MhE2JvAg3Z84cKlSowNSpU6lbty6+vr5kyJAh2vaRF+EyZMjAmjVryJo1Kzdv3sTb2zv5gxc2bfeN3Vx8chGATwt/qnM0iezFC1PVozJldA1FiJjE645OaGgoJ06coFYtU/9SR0dHatWqxaFDh2LcZ+PGjVSqVIlu3bqRMWNGihYtypgxY4iIiIj1PCEhIfj7+5s9RNz4+ZkKnowapW8swkKEhECrVqYkZ8QIqFpV15CESGxRL8IVLlyYOXPm4OHhwfxY5oSKvAi3fv16qlSpQs6cOalRowYlpBunSGQ/HfgJgKrZq1I+a3mdo0lka9ea2h9+qF8cQsQiXonOkydPiIiIIGPGjGbrM2bMyIMHD2Lc59q1a6xZs4aIiAi2bNnC0KFDmTRpEqNHj471PGPHjsXLy8v48JGuNXEWtddF7tz6xSEsxPPnULcuLF+uKuEsWADDh8utPmFTkuMinFyAEwkx9fBU/rzyJwCT6kyyvW5rN26o57JloUgRXUMRIiYJKkYQHwaDgQwZMvDLL79QpkwZWrRowffff8+cOXNi3WfQoEH4+fkZH7dv307qMG3Gkyfq2d0dWrbUNxahs5s3oUoV2LNHlY/+809o21bvqIRIdMlxEU4uwIn4GrVnFN/+9S0AedLksb27OQDTpqnn1q31jUOIWMRrjE66dOlwcnLi4cOHZusfPnxIpkyZYtwnc+bMuLi44OTkZFxXqFAhHjx4QGhoKK6RtdejcHNzw83NLT6hCWDiROjXT7UrVNA3FmEB9u1Ts8Zmzapu9RUvrndEQliMqBfhnJycKFOmDHfv3mXChAkMHz482vaDBg2iT58+xmV/f39JdkSsDt85zLDdw4zLS5sufcPWVmrxYjVpH0AtGyuZLWxGvO7ouLq6UqZMGXbs2GFcZzAY2LFjB5UqVYpxnypVqnDlyhUMBoNx3aVLl8icOXOMSY5ImOBgU5IDUL++frEIC/HVVzB7tiofLUmOsGEJvQiXP3/+WC/Cvc7NzQ1PT0+zhxCxGbprqLEdMSyCitkq6hhNEunSRT3XrAmFCukaihCxiXfXtT59+jB37lwWLVrEhQsX6Nq1K69evTJWYWvdujWDBg0ybt+1a1eePXtGr169uHTpEps3b2bMmDF069Yt8X4KQfv2pvZff8GAAfrFInS0ZAlELfXepYvMayBsnlyEE5bkyrMrbL+2HYBZDWbh6JDkowSS36lTEBSk2pHd14SwQPH+39eiRQsmTpzIsGHDKFmyJKdPn2br1q3GvtG3bt3i/v37xu19fHz466+/OHbsGMWLF6dnz5706tUrxlLUIuF++83UrlNHvziETgwGGDhQ9ZP++GN1i08IOyIX4YQlMGgG6i2tB4C7sztfFPtC54iSyOLF6rl8eekxICxavOfRAejevTvdu3eP8bXdu3dHW1epUiUOHz6ckFOJOIh6AX/DBv3iEDoJCVFFBlasUMsNG4KMcRN2pkWLFjx+/Jhhw4bx4MEDSpYsGe0inKOj6dpe5EW4b7/9luLFi5M1a1Z69erFALkdLt7BotOLuPr8KgAbPt+At7u3vgEllci7p999p28cQrxFghIdYVmiThPRoIF+cQgdPHsGn3wCe/eq8tG//gpt2ugdlRC6kItwQm/rLq4DoHbu2tTJY6PdKw4ehH/+UW2pfCQsnCQ6NmDVKvWcKpX6rivsxI0bqurExYvg6akmbpPKN0IIkew0TWP60en8cekPAKbWm6pvQEll7Vpo3ly1S5SA7Nn1jUeIt5CvxVYuIkKNCQRo0kTXUERya9NGJTnZsqny0cWK6R2REELYpY2+G+m1tRcA9fPWp3D6wjpHlER++snUnjVLJp8WFs8GS4HYl48+MrVr19YvDqGD+fPVHZzDhyXJEUIIHY3ZPwaAUplKMbvhbJ2jSSLPnsHRo6p97x5UrqxvPELEgSQ6VuzBA9i61bT81Vf6xSKSyYULpnaePLBtm5oQVAghhC6uPLvC0bsqAdjy5RZyeOfQOaIkMneuevb2hsyZdQ1FiLiSRMeK9e5tat+7B47y27RdBoOaHKlYMdVNTQghhEXo+WdPAKrnqE6mVDFPUGv17txRUxgAxDChrhCWSr4aW6mwMIhaREgurtiw4GBo2VL1jY6IgHPn9I5ICCEEcOzuMf688icA42uN1zmaJHL5Mvj4mJYjpzIQwgpIMQIrFBIC7u6m5dk22h1YoPpEN2kC+/aBiwvMmwetWukdlRBCCGDcgXEAfJDrAypmq6hzNEnAYIAWLUzLvXtDo0a6hSNEfEmiY4U+/9zU9vGRuXNs1vXrqny0r68qH71uHXzwgd5RCSGEAPxD/Nl0aRMAo94fpXM0SWTePFNp1xUrzJMeIayAJDpWRtNg/XrVdnaGW7d0DUcklXv3oGJFePRIZbNbtkDRonpHJYQQ4j9/+P5BaEQoOb1zUtnHBiuQvXwJnTur9nffSZIjrJKM0bEic+eaFxzYtEm/WEQSy5xZdVkrWVKVj5YkRwghLMrgnYMB+LzI52/Z0krt3Wtq9+unXxxCvAO5o2Mljh0zXViJ9P77+sQiklBYmBqL4+AAM2dCUBCkTq13VEIIIaK48PgCt/xUl4rmhZvrHE0SWLbMNGeFqytkzKhvPEIkkNzRsQIGA5Qvb1qeOxdevVLvPcJGGAzqilnjxhAertY5O0uSI4QQFiYkPIQPF38IQEqXlJTJUkbniBJZQID5xHxTpugXixDvSO7oWAEnJ1O7Sxfo2FG/WEQSCA6G1q1h9Wq1/PffUmFCCCEs1IDtA7gfcB+AHa136BxNEujWzdT+6y+oU0e/WIR4R5LoWLg//zRfnjFDnzhEEnn6VN3FOXBAdVmbP1+SHCGEsFChEaFMOzINgIrZKlIhWwWdI0pkAQGweLFqFyokSY6wepLoWLjt203tgADzuzvCyl27pspHX7oEXl6qfLQMvBJCCIvVdGVTY3vrl1t1jCSJbNtmah86pF8cQiQSSXQs3KJF6rloUUiZUt9YRCI6dgwaNoTHj1X56D//hCJF9I5KCCFELHyf+LL58mYA+lfuj5e7l84RJYF27dRzy5bqApwQVk6KEViwu3dVzyaA2rX1jUUkMmdnCAyEUqVU+WhJcoQQwmL94fsHBWcWNC6Prz1ex2iSyKZN4Oen2k2a6BqKEIlF7uhYqBMnoGxZ0/LgwfrFIpJAqVKqi0DRolJZTQghLNiNFzf4eMXHxuU/v/zzDVtbqdBQaNTItPzpp/rFIkQikjs6FipqktOtG6RLp18sIhEYDDBokLp7E6lSJUlyhBDCghk0A7WXmLpUnPvmHPXy1tMxoiQStfLRkSP6xSFEIpNEx0J5e6vnTz6RSmtWLzgYPv8cxo2Djz82dQ0QQghh0Q7dPsSVZ1cA2Nl6J4XTF9Y5oiQyZox6/ugj84n7hLBy0nXNQr14oZ5Hj9Y1DPGuXi8fPWWKDPAUQggrUXVBVQDq5a3H+7lstCpmYKAqkANqTjchbIgkOhZoa5SKlQ4O+sUh3tHVq2pOHCkfLYQQVmf4ruHGdvdy3XWMJInt2weapvrIy9gcYWMk0bEwL1+qqVUi5cunXyziHRw5ogZ2Pn4M2bPDli1SWU0IIazEk8AnjN0/FoBc3rlomL+hzhEloXHj1PNHH+kbhxBJQMboWJiSJU3tX35RVYiFFfrf/1SSU7q0lI8WQggrc/zeccIMYQCc+vqUztEkocePYfdu1f7yS11DESIpyNdoC/J6N7VOnfSJQySCuXMhWzYYOhRSpdI7GiGEEPEw5fAUADqX7mybE4NGWrjQ1P7wQ93CECKpyB0dC9GsmfnylSv6xCESyGCAFStUP2cADw8YP16SHCGEsDL3X95n29VtAPSv0l/naJJYZBGCEiVkULCwSZLoWIDAQPj9d9OywQB58ugXj4inoCBo0QK++AJGjNA7GiGEEO9g3P5xaGiUz1qePO/Z8IdxcDCsXq3aEyboG4sQSUS6rlmAo0dN7Tt35KKKVXnyRJWPPngQXF2hQAG9IxJCCJEAz4Ke0W5DOzb6bgRgUNVBOkeUxPbuNbWrV9cvDiGSkCQ6OtM0GDBAtdOnh6xZ9Y1HxMPVq6pE3uXLaobX9euhRg29oxJCCBFPEYYI8vwvDy+CXwDQMF9DmhRsomtMSW7gQPX8ySfg5qZvLEIkEUl0dBQUpIZyREqZUr9YRDwdPqzKRz95AjlywJ9/QqFCekclhBAiAXr+2dOY5Hxd5mtmNpipb0BJbds2OPVfNbl27fSNRYgkJImOjl4fznHwoC5hiPh6/hzq1gV/f1U+evNmyJRJ76iEEEIkwB++fzDr+CwAWhRpwZyP5ugcURILC4M6dUzLDW14jiBh9yTR0cGjR2rsemTpegcHVYBAWIk0aWD6dDWI87ffpLKaEEJYqbXn19J8dXMASmcuzfJmy3WOKBkMG2ZqX74MjlKXStgu+evWQe/e5knOpUt6RiPixGCAe/dMy61bw8aNkuQIIYSVCosIMyY5Hi4e7GqzC0cHG/9aFBEB48apdq5ckDevvvEIkcRs/H+05Vm+XN0EiHTtmrzPWLygIPj0U6hSBR4+NK2X8nhCCGGVwiLCcB3taly+1+cenm6eOkaUTLp3N7X379cvDiGSiSQ6yejRI/jyS9Pyzp2QM6du4Yi4ePxYzRb9++/qjs7Jk3pHJIQQ4h3129bP2B5fazxe7l46RpNMDh2COf+NP6pSBbJk0TceIZKBjNFJRj//bN6uWVO3UERcXLmiykdfuaLKR2/YIHMNCCGEldvou5FpR6YB8FmRz+hfpb/OESWT8eNN7VWr9ItDiGQkiU4y8PeHatXg7FnTus6d9YtHxMGhQ/Dxx1I+WgghbMgd/zs0XdkUgKrZq7K8qeUVH9A0NSzUySkRD3rtGvz1l2ofOCB3c4TdkK5rSSgiAoYMAS8v8yRn/XrdQhJxsWsXfPCBSnLKlFFz5kiSI4QQVq/N+jZEaBEUSV+ELS234OSYmNnEuztxQhVBc3ZW43cdHCB3bjhz5h0PXLkyBAdDxYrqIYSdkDs6SUTT1BvV6x48gIwZkz8eEQ8lSqi7OPnySfloIYSwEWP3jWXn9Z0A/PD+D6R2S61zRNGVLWtqX72qnq9fh5Il1cXTBFWCvnvXVEhn1CgpJy3sivy1J5HTp82XjxxRyY8kORZK00zt995T9b/XrZMkRwghbMCOazsYvHMwAO1KtqNpoaY6RwShofDihWn56NE3bx91nG+8HDtmateqlcCDCGGdJNFJIjt2mNqaBuXL6xeLeIugIGjWDGbNMq3LlCnmW3JCCCGszuh9owFwd3ZnfK3xb9k66f3xB7i5qfmnHRzUo0IF9VratDB7tmqXLm3a55tvEniyyIn7ZHCwsEOS6CSBgADo91/lyrRp9Y1FvMXjx2o8zrp16pcWdZ4cIYQQVm/2sdnsvrEbF0cXLve4TPqU6XWN5+lTVesmNvPnQ5cu6iLpiRNqrG+kwMB4niw8HDZvVm25myPskCQ6iezWLXUzIFJ/O6laaZUuX4ZKlVSxgTRpYOtW6VsohBA2ZN/NfXyzRd0KaVuyLdk8s+kaz82bkC5d7K83ahQ9CRoxwtROmRJCQuJxwk2b1BQJqVJJoiPskiQ6iWzOHHj1yrTcs6d+sYg3OHRIJTlXr0KuXHDwoKoBLoQQwmbMOzUPgHJZyvG/+v/TNZaQkOiThO/bp+7c7N+vrr1t3Bh9v9fLTLu7x+Ok332nnjt0UBf0hLAzkugksrFj1bOHh3rzitcbkkgea9eq7mpPn6oSN4cOQcGCekclhBAiET0NfMqiM4sAGFFzBO7O+n4gR/0+0KaN+o5QtaparlJFlZOOzevzexoMqjPC7NmqGluMQkLU/DkAxYolOG4hrJkkOokoLMzUli5rFuzaNTWfQKNGapCmdFcTwibMnDmTnDlz4u7uToUKFTj6tjJW/1mxYgUODg40adIkaQMUyeau/12qL6wOqAIEtXPX1jWex4/NlxcujN/+n34KCxaYlp2cVKeEb76B6dNN68PDo+z09demdsuW8TuhEDZCEp1EtGSJqf3tt/rFId6ib19YuVIVIEiZUu9ohBCJYOXKlfTp04fhw4dz8uRJSpQoQd26dXn06NEb97tx4wZ9+/almnRdtRmvQl+Ra1ouzj8+D8DcRnNxcXJJ1hgiItT8N/v3w+3bkCGD6bWXLxN2zLZtY17/7beq25uDA7i4qOezx0NVaTeApk0hRYqEnVQIK5egREeumsXsxx9NbU9P/eIQrwkMVBXV/P3VsoMDfPZZ9I7PQgirNXnyZDp16kS7du0oXLgwc+bMwcPDg/nz58e6T0REBF9++SUjR44kd+7cyRitSEqj9o4izKC6WPz91d98VfyrJD+npqlpJYYMUR8xzs6QO7ca+pk9u2m78ePfbXq2nTtjXp8/v/lyiXKu7H1WRPWXmzcv4ScUwsrFO9GRq2bRvXih3tgiu8KO179Ev4j06JEajzNxIrRqpXc0QogkEBoayokTJ6gVpaqUo6MjtWrV4tChQ7Hu98MPP5AhQwY6dOjw1nOEhITg7+9v9hCW5+Dtg4w/oD6Ep9efTu08Sd9lLTwcHB1VUbOoFzxf5+5umnoiod5/H86dU+davfrN29ZgL9SvD97e73ZSIaxYvBMduWoWXa5cpranJ3z5pX6xiCguXVKdmI8cgffee/dPGCGERXry5AkRERFkfG28XcaMGXnw4EGM++zfv5958+Yxd+7cOJ1j7NixeHl5GR8+Pj7vHLdIXIFhgbRd3xaA8lnL061ct2Q5b+PGcdvu1i11UfRdFS4M27ZB8+Zw5oz5a8HB5sv389d49xMKYcXilegkx1UzsK4rZ2Fh6o4OQLZs8OABZM2qa0gC4MABleRcu2YqHx1Z3kYIYddevnxJq1atmDt3LuneNKlJFIMGDcLPz8/4uH37dhJHKeJr4PaBXH52GTcnNxY2XohDYmQVb/HoEWzZYlpOmxamTlVd2SIf338PU6ZA+iSYp7R4cdNQ0zNnwM0Nnl8wJffNtn0dy55C2Afn+Gz8pqtmFy9ejHGfyKtmp0+fjvN5xo4dy8iRI+MTmm6i9tj7918Z72cR1qyBr75SpTXLlVMDMqWymhA2K126dDg5OfHw4UOz9Q8fPiRT1Bmc/3P16lVu3LhBo0aNjOsMBgMAzs7O+Pr6kidPHrN93NzccHNzS4LoRWLYcnkL04+q8mMbPt9AofSFkuW8UT9a7t83nzA80ujRSRtDQID5sveGRWSjJXfw4dBJdwIC3m1ckBDWLEmrriXkqhlY15WzDz9Uz05O4OWlbywCVXjg229VktOoEezaJUmOEDbO1dWVMmXKsGPHDuM6g8HAjh07qFSpUrTtCxYsyD///MPp06eNj48//pj333+f06dPS7c0KxSZ5Lg6uVI3b91kOWfUG0Y//hhzkqOLgQNZTGvjYurU6s6SEPYoXnd0kuOqGVjXlTNfX/Uc64RdInl5eMCmTbB0KYwbJ5XVhLATffr0oU2bNpQtW5by5cszdepUXr16Rbt27QBo3bo1WbNmZezYsbi7u1O0aFGz/b3/G7D9+nph+W773Wb7te0AzG44O8nPN2IEvN7pZNCgJD9t3Bw7BkBNdputHjRIfSQKYW/ilehEvWoWWSI68qpZ9+7do20fedUsqiFDhvDy5UumTZtm9VfN7t83tSMTHqGDwEA4cULV8QQoUUI9hBB2o0WLFjx+/Jhhw4bx4MEDSpYsydatW41drW/duoWjo0wdZ4vmnZpHuCGcdB7paFuybZKeq1gx1U09qn79EqfIQKL47DMAHIBrVzVy51GBjR8PY8daUJxCJJN4JTogV82iilrd5PUa9iKZPHqkuqidOaMmGKhcWe+IhBA66d69e4wX3QB27979xn0XxneqemERNE1j9XlVZ3nMB2NwdEi8ZHbmTIj8c2rYEG7cUKWdowoJAVfXRDvlu9mxQwUJMGsWuXI7cOyYGqoKsGIFfPGFbtEJoYt4Jzpy1Sw6Dw+9I7BTvr7QoIGqrPbee9IJWQgh7EzPP3ty/vF5PFw8aF64eaIdd/t2U5IDsHmz+esDBqjuaxaT5Pj5qcl1APLkgS5dAChb1rRJx46S6Aj746Bplv/t0N/fHy8vL/z8/PD09NQ7HKPr19XMxx4e8OqV3tHYmf371eQFz56pX8Kff8ptNSGSgKW+/+pN/l309/uF32m2qhkAXxX/iiWfLEm0Y7//PsR2E/DcOTWXjcWYNQu6RZkz6PRps+7b8+dD5OwevXurUtdCWLu4vgfb162XRLRhg/p+LXSwerW6cvXsGVSoAIcOSZIjhBB2RNM0Bm4fCICbkxvzPp4Xr/3PnFFVUzdvhsGDYcEC+K9WEsHBatgnwMcfq+cUKWDZMtVxwKKSHE0zr4wwYUK0MapNm5raU6equzpPnyZPeELoTe7oJFDGjKY5dFKkUOPhRTLYvVtdagN1R2f5cuk7KEQSssT3X0sg/y76OnLnCBXnVQTgVu9b+HjFvbiRry8ULBj765kyqcm/s2WDmzfVxOAWWwj2zh2ILOz0+DHEMpVHTD9z9uyqZ4qdjTYQNkLu6CShgABTktOzJzx/rm88dqV6dWjWDHr0gLVrJckRQgg7c9f/rjHJ+aTgJ/FKcp48eXOSAyrJAdXdy9HRgpMcgL171XO+fLEmOQAFCqjqcFHduqVmYFi/PunCE0JvkugkQPv2pvaECRb+JmgLXr1SpW1AfeqsWAHTpskcOUIIYWc0TaPr5q7G5RE1R8Rpv4kTVWnl9OlN69KmffM+AwcmIMDkZDDAl1+qdqFCb918zBj4+efo6z/5JJHjEsKCSKITD0OGqDfK1aqSJXnzWlDFFVv18KHqqta+vamqmrOzTAYghBB2aPrR6fxx6Q8AFjReQPGMxd++z/TodzN++EHd3dE080dwMOzbp3pquLsnxU+QiPbtM7WHDn3r5s7O0Lmz+rl//NH8tdfnBhLCVkiiE0e9e0d/Y1i+XJdQ7IevL1SqpGZ6/usv0/wAQggh7NLs47MBaFW8VZwmB92/X3Uxjyp//tjzAjc3qFoV/pvyz7Lt2qWe8+QxryP9FmnTqgIMYWGmdcWKQXh4IscnhAWQRCeOpk0ztXftUm8QkZNwiSSwb59Kcq5fV2/ihw5Brlx6RyWEEEInh+8c5uKTiwBMqjMpTvtE/eyeN0/dtfH1TYrodBBZba1HjwTt7uwMqVKZlseNS4SYhLAwkujEQdTy9EeOQM2a6g1CJJGVK1X56OfPTeWj8+XTOyohhBA6+u7v7wD4INcHpE+Z/o3bhofDp5/CmjVqec8e8/G1Vu/vv03tPHkSfJgnT0ztOPR+E8LqSKITB7NmmdpyFyeJzZgBn38OoaFqhOTOneajR4UQQtidey/vcfjOYQCm1H3zjJd79oCLiynJAdUdzabUrWtq16uX4MO4ucHhw6ZlBwfz5EcIayeJzlvcuWNqHzggY+CTXLFi6hOqZ09V9UHKRwshhN2bdHASBs2Aq5PrGwsQ+PurXhdRnTljY3PFLFhgav/++zt3MSld2nw5fXr45593OqQQFsOW/usniagFCORuTjKoUQPOnpXy0UIIIQDYdnUbU49MBVSltdgYDFCxomm5a1e1rvjbC7NZl67/ldf29EyU2tAuLvDLL+brihdXF3ovXzYvWiCEtZFE5y0iL5yUKKHeDEQie/AA6tSBc+dM6942m5sQQgi7cOzuMeotq4dBM9CkYBM+K/JZrNtOnw4XLqh2nz6q27nN9cJ4+NA0r9xffyXaYTt1ghMnzOcW8vFRFepcXSEiQhVyuHFDJY9CWAtJdN7g/n3T+8mYMfrGYpMuXlSV1bZtgzZtTPPkCCGEEMDnaz/HoBnwcPFgedPlODvG3E2rXTs1DQSoBGdS3IqyWZ/Zqrw2mTKZ375KBKVLqzs4McmRQ3X/y5VLOlsI6yKJzhtE7bf64Yf6xWGT9u6FypXV5aG8eWHFChu89CaEECKh9t7cy7Xn1wDY3mo7KVxSxLidpsHChablLl2SITi9RJaU/v77JDl8mjSwfn309Xfvmi/PmgUvXyZJCEIkKkl0YhESonpVAaRMqSqTiESyYgXUrq3KR1esCAcPqmRHCCGE+M/ck3MB+LLYl1TyqRTrdlHnf/njDxu+ZhYWZvrh3n8/yU7TuLFKHoODzechiqpbN9MdNCEsmSQ6sejXz9SO7PMr3pGmwU8/wRdfqPLRTZtK+WghhBDRHLlzhKVnlwLQokiLGLfRNOjeHQYPVsuenvDRR8kVoQ727lU/tKsrFCqU5Kdzc1MFUF++hOHD1fzdUc2fH32dEJZGEp0YBATA06emZR8f/WKxKRER8Oefqt27N6xaBSli7ooghBDCPoWEh/Dp6k8ByOmdkw9zx9x3vFs3mDnTtLxtW3JEp6PIMrAff5ys9bJTpYIRIyBnTtXTxcvL9FqNGskWhhAJ8m7F121QSAjky2fqtjZkiL7x2BRnZ1i3TnUAbttW72iEEEJYoM6bOnPb/zYAB9sfxMPFfD610FB1nSxyXD6oLublyydnlDqIrE6q41wXGTPCixfQujUsWQK3b6vedP7+kDq1bmEJESu5o/OaJ09MSU7evNCokb7xWL0HD+B//zMte3tLkiOEECJGr0JfsdF3IwC1c9cmc+rMZq+HhUGBAtCqlWldYKDqiWHT/voLHj1SbQv4DB061Hw5srOGEJZGEp1YODurMos2f4UoKZ0/r4oN9OoFc+boHY0QQggLt9F3Iy+CX+Dk4MTmlpujvT5ggCrWGalxYzvpAT1hgnp2dIQMGfSNBdXzZcQI03KLFjKxqLBMkui8Zt489SxTuryjPXugShW4eVO9I9aqpXdEQgghLNzWq1sB+K7Sd7g4RZ+le/FiU3vMGFi7Nrki09nNm+p5/Hh944hi+HDzOzsxlaUWQm+S6ESxbp36jwtq3LxIoOXLoU4d1ZG3UiUpHy2EEOKtngY+5fcLvwNQL2+9aK+vXGkqFPTiBQwaZCeTVz56BFeuqHbr1vrG8pqocxadPKlfHELERhKd//zxh6p2HEn6myaApqkJDb78Uo0WbdYMduyAdOn0jkwIIYSF23plKwGhAeR7Lx/Vc1QHICgI7t+Ha9fg88/Vdl98YV75y+aNHaueixWziG5rUWXJYpqsdcUK6Q0jLI8kOv9ZtMjUXr0a6kW/mCTe5tQp04QGffpI+WghhBBxEhQWxPDdqktF00JNcXJ0wmAADw/1ZTpPHtO2kVWW7caBA+q5aFF944jFp5+qoUM3bsDu3XpHI4Q5KS/9H+f//iVGjoTmzfWNxWqVLg2TJ6t3vJ499Y5GCCGElVj+z3KuPr9K1tRZGVxNXTDbtCn6dhUqQK5cyRycnp49g2PHVNtC57vw8ACDQbU/+ECFfOuWSlBlPnChN0l0/nPnjnpOk0bfOKzO/fsQHm6aVbV3b13DEUIIYV1ehrw03s1pWawlnm6eXL6sKqpFNX489O+vQ4B6ijoIpkAB/eKIh/feM1+W7mxCT9J17T+Rd4alPGI8RJaPbtAA/Pz0jkYIIYQV+n7n99x9eRcvNy+6lO3CvXuQP7/p9VOn1Jdlu0tyIiJUMR+Ahg0tuvLCd9/F/pqDQ/LFIcTrJNEBSpQwtatX1y8Oq7J7N1SurO5Ph4TA8+d6RySEEMLKvAh+wfSj0wGYUncKOTxzkzWr6fVJk6BkSX1i093q1XD3rupbb+F1tCdOfPOdmxkzki8WIaKy+0Rn40Y4e9a0XLasfrFYjcjy0X5+Ktk5eBBy5tQ7KiGEEFYmspy0m5Mbnxb5lMOHTa/lzq3q2tilixdVeTlQ0zS4uekbTxw9eKCug2qaqpQXqUcP1ctdiORm14lOly7mfYDv3dMvFqugaarM5Zdfqj5+zZvD9u1SPloIIUSCrDy3EoAe5XuQyjWVsYR05cqqd7TdatHC1J46Vbcw4itjRqhRQ7Vz5YING0yv2e2dOaEru050fv7Z1N6/HzJn1i8WqzB+vKl8dN++avY2KR8thBAiAQ7fOczfV/8GoFWJVrx4YSoMVKaM1dzESHyXL5u6mvz6q6poaqU+/hhSpVLtc+dg1Ch94xH2x24Tnfv3Te29e6FKFf1isRqtW6suatOnw4QJqoy0EEIIkQALTy8EoEG+BhTPWJy//za9Nm6cPjFZhF691HO6dNCunb6xJIJ//jG1hw1TE7/u3atfPMK+2OU3VU1T9d0jFSumXywWLyjI1M6SRV2S6d5dv3iEEEJYvQO3DjDv1DwA+lTsQ0gIjBihXvv2WzU3i93ZuBGyZYM//1TLc+faxAXFnDnNaymsXKm6t928qVtIwo5Y//+gBJg+3dQuUQK8vXULxbKdOweFCql3pUh2+ekjhBAisQSHB/Pxio8JN4RTN09dPsj1ATVrwoUL6nULnRczaV25ogYN372rlmvUgCZNdA0pMTVtqr5SRLVkiT6xCPtil4nOnj2m9qFD+sVh0XbtUv35bt6EMWOkXIoQQohE8eHiD3kW9AyAmQ1msmCBg7HaWocO0SectAuffGJq//STurtjYwoXVl8tIg0dqubY+flnmVRUJB27THR+V9Us+eorGUsfo6VLoW5dVT66alXYuVPV8RdCCCHewZE7Rzh4W02CObvhbNI55aFDB9PrkyfrFJierlyBf/9V7REjoF8/8PTUNaSkUrOmeY94UBVw583TJRxhB+wu0Rk+3NRu0EC/OCySpsGPP0KrVqp89GefwbZtkDat3pEJIYSwAf239wfgw1wf8lXBLmZdxy9fttnv929Wr56p/f33+sWRTNzdoWdP83UyoahIKnaV6Lx4AT/8YFqOWqbe7hkM0LmzqXN0377w22/qHUkIIYR4R7f9brP3piq3NanOJLPv9wB58+oQlJ78/dWkoFevquVvv7Wb3hPTpoGvLxw/rpbPnIHHj/WNSdgmu0p0Fi0ytf/5xyaKmSQeR0fVMdrRUV1akfLRQgghEtHw3apLRf60+bl9vDgHDphee/RIp6D0NGIErFih2kWLwqRJuoaT3PLnN58iKEMGVSDK31+/mITtsatvslGLhxUtql8cFmvsWFWdoVs3vSMRQghhQ26+uMmC0wsA0H77nUaNHIyvBQRA+vR6RaaTXbtgyhTVTpFCzVru4PDmfWyQgwN8951p+exZNX2QFCcQicWuEp3ISUKbN9c3Dovx77/QsiUEB6tlR0coX17fmIQQQticvtv6qsbDolw+WMS4/u5dSJlSp6D09L//mdoXLoCXl36x6GzCBPPlsDDVi0+IxGBXic6NG+o5ahVHu7Vzpyof/dtvqsajEEIIkQQuPb3E2vNqxship7cZ1x85Yj55t13QNNWPfv16tfzjj5Ajh64h6c3BAUJDYfly07pp0+DlS/1iErbDbhKdrVtN7dy59YvDIixZoqq8+Pur8tGDBukdkRBCCBukaRq9t/ZGQ6PIy578cygTAEeP2mEHglevVM+Jtm1N6zp31i0cS+LiouoyRK3G5ukJe/fqF5OwDXaT6NSvb2rb3ZtrJE2D0aOhdWvz8tF2OTubEMLWzJw5k5w5c+Lu7k6FChU4evRorNvOnTuXatWqkSZNGtKkSUOtWrXeuL1ImB3Xd/DnlT/h/CecmzTNuL5MGR2D0kufPubLK1aoASnCaNo0qFPHtFyjBvj4yN0dkXB2kehE3iEG6NrVTouJhYVBp06mbmr9+kn5aCGEzVi5ciV9+vRh+PDhnDx5khIlSlC3bl0exVLOa/fu3XzxxRfs2rWLQ4cO4ePjQ506dbh7924yR27bZhydAQYHvA9NN65r1MgOP4fDw+GXX1S7YEF14VHmuIjR5s3my3fuwIcf6hOLsH4Ommb5tS38/f3x8vLCz88PzwTMJvbBB6rACag7xx4eiRygNbh+XV1C8/OD6dPhm2/0jkgIYQXe9f03uVSoUIFy5cox47+ZBw0GAz4+PvTo0YOBAwe+df+IiAjSpEnDjBkzaN269Vu3t5Z/Fz0dvXuUCr9WgPNNYZUao9OlC8yerXNgye3hQ5XcvHihlg8cgMqVdQ3J0hkMagLZggVN68LC7GaaIREHcX0PtvlrKosWmZKcAQPsNMkByJULNmyAdeskyRFC2JTQ0FBOnDhBrVq1jOscHR2pVasWhw4ditMxAgMDCQsL471YuvKGhITg7+9v9hBv9uO+HyEkFW5//QpA9+52mOQEB6uLjJFJTurUUKmSriFZA0dHKFAATpwwrdu/X794hPWy+UQn6pi/OFyksy3//gu7d5uWq1WDjz/WLRwhhEgKT548ISIigowZM5qtz5gxIw8ePIjTMQYMGECWLFnMkqWoxo4di5eXl/Hh4+PzznHbsnsv77Hl8hY42ZEQvzRkyACDB+sdlQ6GDFE1tEFNGOPvb5fz5SRU6dIq4QF4/33TNCFCxFWCEh1rHPA5cSIULpzsp9XPjh2qfHTjxirhEUIIEaNx48axYsUK1q1bh3ss4xYHDRqEn5+f8XH79u1kjtK6TDs8jXD/NPCXmhTzu+8gc2adg0puQ4bApEmq/fXX6ouIiLexY03tLFnUBLNCxFW8Ex1rGvAZeacYoGbNJD+d5Vi82FQ+umRJO5yoQAhhT9KlS4eTkxMPHz40W//w4UMyZcr0xn0nTpzIuHHj+PvvvylevHis27m5ueHp6Wn2EDE7/eA0q86vgrWmiVE++0zHgPRw6ZKaIwegQgU77LOXeF6f+zB1apg/X59YhPWJd6IzefJkOnXqRLt27ShcuDBz5szBw8OD+bH81S1btoxvvvmGkiVLUrBgQX799VcMBgM7dux45+DfZsgQU7tUqSQ/nf40DUaNgjZtVIWXzz+Hv/+W8tFCCJvm6upKmTJlzD5XIj9nKr1hPMRPP/3EqFGj2Lp1K2XLlk2OUG3ejRc3KD+3PDce38fhXgVATRWTM6e+cSW7Dh3Us5MTbNki3dXeUVgY9OplWu7QQbqxibiJV6KTHAM+IfEGfa5YoZ69vOyglGVYGHTsCMOGqeUBA2DZMnBz0zcuIYRIBn369GHu3LksWrSICxcu0LVrV169ekW7du0AaN26NYOiTI48fvx4hg4dyvz588mZMycPHjzgwYMHBEi/mHey7OwywgxhpD7xA1pIajJnVoU+7crIkaaR81OnysXGRODsrP4po05FlCWLuqYrxJvE6+t/cgz4hMQZ9Ll6NTx9GnnOeO9ufWbMUPdyHR3VLfJx4+wguxNCCKVFixZMnDiRYcOGUbJkSU6fPs3WrVuNn1e3bt3ifpRLwLNnzyY0NJTmzZuTOXNm42OijKNIsHBDOP87OAeW/cHLrf0BaN4cXF11Diw53b8PI0aodvr0amyOSDSTJkG+fKblCRMgJES/eITli9c8Ovfu3SNr1qwcPHjQrDtA//792bNnD0eOHHnj/uPGjeOnn35i9+7db+wLHRISQkiUv1x/f398fHziNV9B1LvEDx7Aa7mZ7QkNVZ2gO3aEjz7SOxohhI2Q+WJiJv8u0Y3bP45B007BmpXGdefO2VkhoHLl4Phx1Q4JsbMsL3ncuwdZs5qvO3sWihXTJx6hj7i+B8dr6qXEGPC5ffv2NyY5oAZ9ur1Dl6vgYFN77lwbTnKuXlUdn52c1Jvp+vV6RySEEMIOBYQGMOHgBDj3s3Gdry/kz69jUMlt/35TkvPBB5LkJJEsWVSyE7XOUvHi6nqvi4t+cQnLFK++TdYy4PPaNVP788+T/HT62L5dFZj/9ltVhEAIIYTQyepzq3l2Kz1caA7AmTN2luRs2KDmqou0caN+sdiBzJnh+nXzHvorV8a+vbBf8R7EYU0DPr28IFWqJD9N8lu4EOrXV+Wjz5yRDqpCCCF08+vJX+m6uStsnQbAhx+qK+x2IzAQmjRRbScnePgQUqbUNSR7kDMnRERA3rxqeehQdVdHiKjinehYw4DPyNmXY5n3zXppmqrm0q6dKjXyxReqfLTN/aBCCCGswcuQl3T6oxMhd/PD1boATJmic1DJ5flzNSA4alJz6hRkyKBfTHbot9/U840bqtDsuHG6hiMsTLzG6ETq3r073bt3j/G13bt3my3fuHEjIadIsIgIdQcZwMMjWU+dtMLC1GQECxeq5YED1WRkUllNCCGETn468BMEeeFx8CcCgaZN7WhQ+KhR5svt2tnRD285ypZVF7jHjFHLgwbB+++reVqFsLlvyRcumNr79ukXR6L79FOV5Dg6wpw5MHasJDlCCCF0Y9AMTD48GZZvJvBsPQA6ddI5qORy/brp1lXVqqr7WiwTp4uk9+OPqid/pLlz9YtFWBab+6YcEWFqv15+0Kq1awepU6sBjlKXXwghhM42X9pM4K5ucLsKAFu2QL16OgeVHLZvh9y5TcszZkCKFPrFIwA1LmzpUtWeNw9WrdI3HmEZEtR1zZK9fKmeM2fWN45EER6upgMGaNxYXUFKm1bfmIQQQti9sIgwPv2tFWx/YVxXv75+8SSr2rVN7fHjoUQJ/WIRZipXNrVbtIBmzVR9CGG/bO6Oztix6tnqC5Ft2wZFisDNm6Z1kuQIIYSwAGP3jyXkQGfj8qVLOgaTHMLD4c8/IXt207pjx6B/f/1iEtHkygULFpiWmzfXLxZhGWwu0YkctlK0qL5xvJMFC6BBA/XJ8eOPekcjhBBCGAWFBTFuQihs/wlQn7v58ukcVFKKiIBSpdTn8u3bal3RomoUvLA4bduqorSg5lE/f17PaITebC7R2bRJPbdurW8cCaJpMGIEtG+vrh61bAnTp+sdlRBCCGG0/twWgjaPNi4/fapjMMmhUiX491/V/vBDWL0azp7VNybxRpMmmdpFikCUWU+EnbGpROfePVM7Sxb94kiQ0FCV4IwcqZa//16NqnNz0zcuIYQQ4j8RhgiGzztgXN61C7y99Ysnye3Zo7qogZriYft21R/KwUHfuMQbZc4My5eblitWBD8//eIR+rGpROfuXVPbqiq/+PtDw4aqfLSTE/z8M4weLW+kQgghLMrvp7dzecZkADp0DaBmTX3jSXLDh5vaP/+sXxwi3lq0gIIFVfvWrejTHgn7YFOJzi+/qOfs2a0sR3B0hGfP1OzKf/yhrhoJIYQQFiTcEM6gib7G5SH9U+kYTTIIDYUTJ1T7f//TNxYRb46Oam7F1avV8owZEBCgb0wi+dlUohM5RtDfX9844i1VKjW4aN8+O6rPKYQQwpp8u2oSVzc1BaBGHX9y5tQ3niQTHKz6whctavpmbJUDfwWoEtM5c6pqvJ99Bpcv6x2RSE42lehE3sWZOlXXMOLm779h8mTTcubMqqqLEEIIYYEWTioAL7ORJpMfa5d76h1O0jhzRk3+mTWr6Rvx2rXg5aVvXCLBHBzUBKIuLqpCeMGC6lnYB5tKdMLC1LPFd1ubP1+VqfzuO/jrL72jEUIIId5oz/X9BPzzPgCzprva5rRuR49CyZKmZQcH+PhjaNpUt5BE4vjgAzh0CFKnBoNBfQVbsULvqERysKlEZ8cO9axp+sYRK02DYcOgQwdVl/+rr+D99/WOSgghhIhVWEQYbUfshBAvnFME0rxJCr1DSny3b0P16qrt7q7KyRkMsGGDvnGJRFOmDOzda1r+4guVyzo4yFw7tsymEh1XV/WcO7e+ccQoNBTatDGV/fj+e1i82BS0EEIIYYGGbpzJjbVfA9C1ewjOzjoHlNhCQlQVo5AQtXz8OLZfTs4+lSypqoWnTm2+vkgRePBAl5BEErOpt6vQUPVscQMk/fzUre+dO1X56NmzoVMnvaMSQggh3uj03XOMb9obgEw5XzBmWBp9A0oKS5aY2nPnqm+9wmaVLauKVh06BI0bw+PHan3+/DB0qJq+MDQU2rXDNrto2hmbSXQiJy0GddfZomzdqpKcVKlg1SqprCaEEMIqtPn6ubH926JUpLK1itJnz5ouPBYsCB076huPSDaVKsGjR7BoEbRtCy9fQv/+ptePHoWVK61g3Ld4I5vpulasmKmdPr1+ccSoRQv46SfVOVSSHCGEEFYgIDSAf/bmASB9llfUrG4z10aVCxegRAnT8qxZ+sUidNOmjSqw16iRKlqQN69av3q1msddWDebSHQiu9WCqgppEXbuhCdPTMv9+kn5aCGEEFaj9rcr0F5mBtTYfJvTpYupvX27FAeyY3nzwsaNqqjV5cvw449q/eDB8PSpvrGJd2MTic6xY6a2RQwmmzcP6tRRZSmDgvSORgghhIiXGX//weEFnwBQosZNiuRPqXNEiSgsTA3GiCzBtXo1fPihvjEJi/Ldd6o+xYMHkC6dGrolrJNNJDonT6rnlCnBU885zDRNvXl27KjKR+fLp4oPCCGEEFbiwctH9GyTA4LSkiHvHY5ty6F3SIkjPFwVBnJ1hdGj1bpPPoHmzfWNS1gcNzfYvNm0PGQI+PrqF49IOJtIdB49Us+vXukYRGT56Mg3z6FDVedOKR8thBDCinw9Zi/ag+LgFMKR7ZlxcdE7okTyww+wbp35uokT9YlFWLyiRVU3tlSp1PfM0qXh99/1jkrEl00kOpE3TXQrlvLiBdSrp0pUOjmprms//CClOoQQQliVzWcO8ceCggDU+/IyOXPYSK+Exo1N89ilS6emfTAYLHTiPWEp8uZVk4mWKAGBgdCsGXz+ud5RifiwiUQnkm43T9q2VSM1U6VS9zrbt9cpECGEECJh/nlwnqafB6A9LIqzx0v+Nzyf3iG9m5s3oXp1SJNGjTQHqFZNDbzw9JSLkSJOfHzUHLKtW6vllStVD0hhHWwi0Yk615cuJkyA4sVh3z6oW1fnYIQQQoj4mbRlDcXLviT0Ym0Afl0YTL7cbjpHlUAHDqg5cXLmVJ/LL16o9Q4OqrqajJ0V8eTsrEYjZMigltetU3m0sHw2kehkzaqeQ0OT8aT375va+fLBqVNQsmQyBiCEEEK8G03TaDZhCn0/qwZ3K4BDBN8Ne0SbTy1tQro3iIiALVugfHmVzFStaj5yvHFjNXF3UJCMmxUJ5uAAhw+blles0C8WEXc2MftX5N3nevWS6YRz50LPnrB+vekOjqNN5IxCCCHsSL9li/h9YDcwuOLu/YKDu1JTqmQGvcOKu3v3TFc7oypYEL7+Grp3V5fjhUgEuXLB5MnQpw8MHKj+tBo0gNSpIVs2vaMTMbGJb+f79iXTiTRN1Rjs3BmCg019foUQQggrs+bwYSZ1bgQGV9LnucO1896UKmkl3bpCQ2HaNPMkx9FR3bl59gwuXIDevSXJEYmud2/T3LJ9+0LhwpAjB3TqpKqy/fuvruGJ11h9omMwmNpJekc6NFSNRIucLnfYMJgxIwlPKIQQQiSN50HP+Xb0JQhKi5v3E3ZvykjmzHpHFUcGA9Soob5xRvrf/1QXtrp1VfEBIZJI5FCv3r3VfDspU6o/yV9/VVXZihVTyc8nn5jPxSP0YfWJzqZNpnbFikl0ksjy0UuXqqtD8+bByJFSsUUIIYTV+ffBRXLWX8+dzaqMVJ+BARQuaAWT5SxerKqaOjmZBktUrqx6V/TooW9swq44OsKUKWr+xpcv1Z2cYsVMr1+4oEY3fPSRuvuzYoUq9ieSn9Xf0406MCxt2iQ4wYsXUKWKKqSeOjWsWQN16iTBiYQQQoikdev5PUp/cJOwC+0AaNL6Nj/2z6lvUG9jMKgBERMmmK9v3hxWr9YnJiEwFfD75BP10DS4dg3OnoXhw+Gff2D3bvVwd1c5ee3aekZsf6z+jk6kXr2SqB6AlxdUqgRZsqjBQJLkCCGEsEIGzUC93usJu6CK6EyY/pzfF/pYfueEPHnMk5yxY9U3R0lyhIVxcFB/rp98opKds2eha1fImFEN7a5TB+bP1ztK+2L1d3QiJfobtaapgzo4wOzZ8OQJ1tOBWQghhDAZv/k3fvjJj8C93wDwSUdf+nYvoHNUcbB5M9y4YVr29YX8+XULR4j4KFYMZs1Sw7vLlYOrV6FDB9VJqEAB9dUye3Z107J6dVXUQCQuq0901q9PgoP+8gv8+ae6WuTsDC4ukuQIIYSwSqPWrWBYqw/gVUYA0uV4zIKJFp4sGAzqG2LUsTcRETKVg7BKadLA6dNQooTq2jZpUvRtvL1VQtS+vermJhKH1Sc6Fy6o58iJj9+JwaDKR48dq5Z/+w1atUqEAwshhBDJb9ymFQxrVwleZSRlhkeM+yEVndqmx81N78hiEREBf/+tJieJasECSXKEVUuVSpWe/v13OHBAfX/NkEHNP79vn/oe262bqrnRrh3kzg3Fi6tubyLhrDrRCQkxtVu3ToSDtW8Py5er5REj4Kuv3vGgQgghhD5a/bCJpcM/B8Dd+zn7tnlRqrgFZjiBgeoC48qVcPly9NcXL5aLjsImpEgBX36pHlEFB6sK6d9/D0eOqAeoYgeffaZubFasKMV+E8KqE52oc+iUKfMOB3r+XI0c27NHdVWbOxfatn3X8IQQQohkd/zifboO9eX4mo8ASPmeP+dPeZM9uwV+S/rjD/j44+jrM2RQvSo++CD5YxIimbm7Q//+ahqo+fNV97YrV+DiRfXf4LffYNQo1elIxI9VJzpRJTjLvXkT6tdX9xBTp4a1a6X2nxBCCKtiMGhMXHaKXxe94vLuChBR0/ja+dMpye5jgUlOSIiaYTFS2rQwZgxUrapmXBTCzpQoAdOmmZZPnYIuXeDoUdXR6Pp1lRAVsII6IpZCOrw+fqySnaxZYf9+SXKEEEJYld/+vohHtisMaF2ayzuqQYQrnvnOMPznY4SFG8ju46R3iOb8/VU3DHd3CAtT66ZPVyWoOneWJEeI/5QqBQcPQsuWavja/PlQurT6ryLixmbu6CRY2bLq1nn+/JAtm97RCCGEEHH215HrfNk0Ldqr9OAUSu5qR2nXyp3Bbcvg6Ghhd3F27VLf2F6fIr5AAfj6a31iEuL/7d17TFTXvgfwLwzODDaAej3AoNOqiLVRKq3WEdRy9ZDDPT5ak9vIUYO0qbWN2Hgkpb6lrVassY2ppRrUqqfBYqXqbZRjq1hrRDwqQo7xgVFQkTpUq8JcXwiz7h/rzuDooOyB2fPg+0l2wmzWht/8HPePNXvPb3k5jQbIy5ONCoYPlx9pMxoBk0n+6frSS7JltVbr6Ui9k09PdNasaf5aUTOWdeuAuDjZ1BzgPcBEROQzfr95G+nLjqOoMAQ3KwYCTVpouvyG40c1iIsZ4enwmjU0yPZSlZXy8bRpjt+PiZHr5MTEqB8bkY9JSAAOHgQmTQJqauTHyn/9VX4vL0/e9RkbK1tZUzOfvnWtpqb562eeacUBViswb568ND5uHFBb67bYiIhIXTk5OejVqxf0ej1MJhOOHj36xPHbtm1D//79odfrERsbi8LCQpUidc2Va/WYmPkrDM/9LwpWJuLm6ZeBJi26DSzFvv2NiIvxUB/aY8fknRG27W9/kx+c1enkG4nTpjlOcmbPlj11z53jJIdIgZEjgcuXgRMngG+/BTIz5d+/xcVAYiLQrRsQHQ1s2CA/3/Nw066Oyqev6NgaEHzwQSsG378vO6nl58vHM2fKri5EROTztm7dioyMDKxduxYmkwmrVq1CcnIyKioqEO7kXH/48GFMmjQJ2dnZGDduHLZs2YIJEybgxIkTGDhwoAeegXM36u/i7yuOoeAf4bhb3R9AIgAgoPMNvDbt33h30rP467C2tB110d278q+pzEzZG/dpxo+XRXvKFNkvl4hcEhgob1d76SX5+I03ZKOCf/5TPq6sbH5foXNnuX6PTuf+rWtXwGAAwsK8qw12gBBCeDqIp6mvr0dYWBjq6uoQGhpq3//BB3J12cxMYMWKJ/yAGzdk++iDB2X76PXrgbQ09wdOROTjWjr/ehuTyYRXXnkFX331FQDAarXCaDTi/fffx9y5cx8bn5KSgtu3b2PXrl32fcOGDUNcXBzWrl371N/XnnmxWgUumeuwde8F/M8eC6qrdLj1ewju3eyGpvpwwNr8nqSm6xUMH38em7Lj0Duqy9N+MFBdDbSlzDc0AAUF8pPQgPxZubmOt1QA8q3kvn2bHzc1yV64o0cDXvy6IfIXVqtsQ33sGHDqlFyLx2JRPw69HoiMlBOfsDC5hYY2f20wAL17A716ya1zZ9d+T2vPwT59RadVLl6U7aPPnpWZ/uEHICnJ01EREVE7aWhoQGlpKebNm2ffFxgYiKSkJJSUlDg9pqSkBBkZGQ77kpOTsXPnTqfj79+/j/sPrVJdX1/fpphnjPgGxaUDcK0xAn80RqIBXQA4vzJjCLqE6X/6Fm8bimEMtgAXALTmokhxcZtibJWxY4FZs2Rd9aa3cYk6mMBAx8VIGxtlO+p79+RNTUo3pcf98QdQVyePu3hRbk8zaBBQXu7GpKAjTHSWLJGTnJ49gcJC+UktIiLyG9evX0dTUxMiIhw/oxIREYGzZ886PcZsNjsdb360I9j/y87Oxscff9w+AQP4vUaHf98zOezrihtIwj6MwCEYUY0eqEFPXEFkoxmBVwVwtQ2/0NW3TQH5V0x4uOPCnhERcoEPg6ENQRGRuwQFqf8RuLt3ZVNFsxm4dUtOfOrqZEf5ujq5r6ZGTsCqquQVHXfz/4nOl1/K63lLl8q1coiIiBSaN2+ewxWg+vp6GI1Gl3/epMz/wJDSf6BXd6BvhAb9IjohNDgIsiz/Z5vjtYuJ4Rt8RKSK4GB5W1rv3q0b/9BFcrfxz4nO/v3AqFHyMvozzwAbN3o6IiIicpPu3btDo9Gg9pFOmrW1tYiMjHR6TGRkpKLxOp0OOp2ufQIG8N8z/qvdfhYRkS9qx1Nqi3y6vfRjrFZgzhzgz3+WV3CIiMjvabVaDB48GEVFRfZ9VqsVRUVFiI+Pd3pMfHy8w3gA2Lt3b4vjiYjI9/jPFZ1792T76K1bPR0JERGpLCMjA2lpaRgyZAiGDh2KVatW4fbt23jrrbcAAFOnTkWPHj2QnZ0NAJg1axYSExPx+eefY+zYscjPz8fx48eRm5vryadBRETtyKUrOt62KFvw3RvAX/4iJzlBQcDmzcCiRe36O4iIyHulpKRg5cqVWLx4MeLi4lBeXo49e/bYGw5cvnwZV682f5o/ISEBW7ZsQW5uLgYNGoSCggLs3LnTq9bQISKitlG8js7WrVsxdepUh0XZtm3b9sRF2V599VWHRdk+++wzRYuyPWkdnR8+r8K/uv0V4TcqZPvo7dvlrWtERNRmvrKOjtqYFyIiz2ntOVjxREeNRdmcrVdgNBofezLzZt1B+pf90BM1bB9NROQG/IPeOeaFiMhzWnsOVnTrmm1RtqSHFtxszaJsSY8s0JmcnNzieECuVxAWFmbfWmrh+aBTZyzCEtSExwFHjnCSQ0REREREABROdJ60KFtLi6wpXZQNkOsV1NXV2bfq6mqn4954A0jIfQs1P/yLa+QQEREREZGdV3Zda+16BcOGyQ3Quj0mIiIiIiLyHYqu6KixKBsREREREVFbKZrocFE2IiIiIiLyBYpvXfPEomy2xnD19fVKwyUiojawnXcVNuj0e6xLRESe09rapHiik5KSgmvXrmHx4sUwm82Ii4t7bFG2wMDmC0W2RdkWLlyI+fPnIyYmRvGibBaLBQBa7L5GRETuZbFYEBYW5ukwvAbrEhGR5z2tNileR8cTrFYrfvvtN4SEhCAgIMC+37a+TnV1NdcxAPPxKObDEfPRjLlw9KR8CCFgsVgQFRXl8CZWR9dSXXIHvl4l5kFiHiTmoWPnoLW1ySu7rj0qMDAQPXv2bPH7oaGhHe4f+EmYD0fMhyPmoxlz4ailfPBKzuOeVpfcga9XiXmQmAeJeei4OWhNbeLbc0RERERE5Hc40SEiIiIiIr/j0xMdnU6HrKysVi0u2hEwH46YD0fMRzPmwhHz4d347yMxDxLzIDEPzEFr+EQzAiIiIiIiIiV8+ooOERERERGRM5zoEBERERGR3+FEh4iIiIiI/A4nOkRERERE5Hc40SEiIiIiIr/j9ROdnJwc9OrVC3q9HiaTCUePHn3i+G3btqF///7Q6/WIjY1FYWGhSpGqQ0k+1q1bh5EjR6Jr167o2rUrkpKSnpo/X6P09WGTn5+PgIAATJgwwb0BqkxpPm7duoX09HQYDAbodDr069fPb/7PKM3FqlWr8PzzzyM4OBhGoxGzZ8/GvXv3VIrWvQ4ePIjx48cjKioKAQEB2Llz51OPOXDgAF5++WXodDr07dsXmzZtcnucHRlrncQaJ7G2sZ7ZsJa1kfBi+fn5QqvVim+++UacOnVKvPPOO6JLly6itrbW6fji4mKh0WjEihUrxOnTp8XChQtFp06dxMmTJ1WO3D2U5mPy5MkiJydHlJWViTNnzog333xThIWFiStXrqgcuXsozYdNVVWV6NGjhxg5cqR4/fXX1QlWBUrzcf/+fTFkyBAxZswYcejQIVFVVSUOHDggysvLVY68/SnNRV5entDpdCIvL09UVVWJn376SRgMBjF79myVI3ePwsJCsWDBArF9+3YBQOzYseOJ4ysrK0Xnzp1FRkaGOH36tFi9erXQaDRiz5496gTcwbDWSaxxEmsb65kNa1nbefVEZ+jQoSI9Pd3+uKmpSURFRYns7Gyn4ydOnCjGjh3rsM9kMol3333XrXGqRWk+HtXY2ChCQkLE5s2b3RWiqlzJR2Njo0hISBDr168XaWlpPl8MHqY0H2vWrBF9+vQRDQ0NaoWoGqW5SE9PF6NHj3bYl5GRIYYPH+7WOD2hNROdDz/8UAwYMMBhX0pKikhOTnZjZB0Xa53EGiextrGe2bCWtZ3X3rrW0NCA0tJSJCUl2fcFBgYiKSkJJSUlTo8pKSlxGA8AycnJLY73Ja7k41F37tzBgwcP0K1bN3eFqRpX8/HJJ58gPDwcb7/9thphqsaVfPz444+Ij49Heno6IiIiMHDgQCxbtgxNTU1qhe0WruQiISEBpaWl9lsCKisrUVhYiDFjxqgSs7fx53Opt2Gtk1jjJNY21jMb1rL2EeTpAFpy/fp1NDU1ISIiwmF/REQEzp496/QYs9nsdLzZbHZbnGpxJR+PmjNnDqKioh4rkL7IlXwcOnQIGzZsQHl5uQoRqsuVfFRWVmL//v2YMmUKCgsLcf78ecyYMQMPHjxAVlaWGmG7hSu5mDx5Mq5fv44RI0ZACIHGxka89957mD9/vhohe52WzqX19fW4e/cugoODPRSZ/2Gtk1jjJNY21jMb1rL24bVXdKh9LV++HPn5+dixYwf0er2nw1GdxWJBamoq1q1bh+7du3s6HK9gtVoRHh6O3NxcDB48GCkpKViwYAHWrl3r6dBUd+DAASxbtgxff/01Tpw4ge3bt2P37t1YsmSJp0MjolboqDWOtU1iPZNYyx7ntVd0unfvDo1Gg9raWof9tbW1iIyMdHpMZGSkovG+xJV82KxcuRLLly/Hvn378OKLL7ozTNUozceFCxdw8eJFjB8/3r7ParUCAIKCglBRUYHo6Gj3Bu1Grrw+DAYDOnXqBI1GY9/3wgsvwGw2o6GhAVqt1q0xu4sruVi0aBFSU1Mxbdo0AEBsbCxu376N6dOnY8GCBQgM7FjvCbV0Lg0NDeXVnHbGWiexxkmsbaxnNqxl7cNrn7FWq8XgwYNRVFRk32e1WlFUVIT4+Hinx8THxzuMB4C9e/e2ON6XuJIPAFixYgWWLFmCPXv2YMiQIWqEqgql+ejfvz9OnjyJ8vJy+/baa69h1KhRKC8vh9FoVDP8dufK62P48OE4f/68vSgCwLlz52AwGHyyKNi4kos7d+48VgBsBVMI4b5gvZQ/n0u9DWudxBonsbaxntmwlrUTz/ZCeLL8/Hyh0+nEpk2bxOnTp8X06dNFly5dhNlsFkIIkZqaKubOnWsfX1xcLIKCgsTKlSvFmTNnRFZWll+03LRRmo/ly5cLrVYrCgoKxNWrV+2bxWLx1FNoV0rz8Sh/6EzzMKX5uHz5sggJCREzZ84UFRUVYteuXSI8PFwsXbrUU0+h3SjNRVZWlggJCRHfffedqKysFD///LOIjo4WEydO9NRTaFcWi0WUlZWJsrIyAUB88cUXoqysTFy6dEkIIcTcuXNFamqqfbytvXRmZqY4c+aMyMnJYXtpN2Ktk1jjJNY21jMb1rK28+qJjhBCrF69Wjz77LNCq9WKoUOHiiNHjti/l5iYKNLS0hzGf//996Jfv35Cq9WKAQMGiN27d6scsXspycdzzz0nADy2ZWVlqR+4myh9fTzMH4rBo5Tm4/Dhw8JkMgmdTif69OkjPv30U9HY2Khy1O6hJBcPHjwQH330kYiOjhZ6vV4YjUYxY8YMcfPmTfUDd4NffvnF6bnAloO0tDSRmJj42DFxcXFCq9WKPn36iI0bN6oed0fCWiexxkmsbaxnNqxlbRMgREe9lkVERERERP7Kaz+jQ0RERERE5CpOdIiIiIiIyO9wokNERERERH6HEx0iIiIiIvI7nOgQEREREZHf4USHiIiIiIj8Dic6RERERETkdzjRISIiIiIiv8OJDhERERER+R1OdIiIiIiIyO9wokNERERERH7n/wB7BlGFdh6P2gAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "from lightgbm import log_evaluation, early_stopping\n",
    "\n",
    "train_lgb = lgb.Dataset(train[model_cols_inited].values, train[\"def_pd1\"].values)\n",
    "\n",
    "eval_lgb = lgb.Dataset(test[model_cols_inited].values, test[\"def_pd1\"].values)\n",
    "oot_lgb = lgb.Dataset(oot[model_cols_inited].values, oot[\"def_pd1\"].values)\n",
    "\n",
    "callbacks = [log_evaluation(period=100), early_stopping(stopping_rounds=30)]\n",
    "params = {\n",
    "    \"task\": \"train\",\n",
    "    \"boosting_type\": \"gbdt\",  # 设置提升类型\n",
    "    \"max_bin\": 6,\n",
    "    \"objective\": \"regression\",  # 目标函数\n",
    "    \"metric\": {\"auc\"},  # 评估函数\n",
    "    \"num_leaves\": 9,  # 叶子节点数\n",
    "    \"max_depth\": 4,\n",
    "    \"n_estimators\": 100,\n",
    "    \"learning_rate\": 0.1,  # 学习速率\n",
    "    \"feature_fraction\": 0.6,  # 建树的特征选择比例\n",
    "    #     'reg_alpha':50,\n",
    "    \"bagging_freq\": 5,  # k 意味看每 k 次迭代执行bagging\n",
    "    \"verbose\": -1,  # <0 显示致命的，=0 显示错误 (警告)，> 显示信息\n",
    "    \"unbalance\": True\n",
    "    #     'colsample_bytree':0.5\n",
    "}\n",
    "gbm = lgb.train(\n",
    "    params, train_lgb, num_boost_round=200, valid_sets=[eval_lgb], callbacks=callbacks\n",
    ")  # 训陈数据需要参数列\n",
    "train[\"y_p\"] = gbm.predict(train[model_cols_inited])\n",
    "test[\"y_p\"] = gbm.predict(test[model_cols_inited])\n",
    "oot[\"y_p\"] = gbm.predict(oot[model_cols_inited])\n",
    "\n",
    "plot_roc_ks(train[\"y_p\"], train[\"def_pd1\"], \"Trian\")\n",
    "plot_roc_ks(test[\"y_p\"], test[\"def_pd1\"], \"Test\")\n",
    "plot_roc_ks(oot[\"y_p\"], oot[\"def_pd1\"], \"OOT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T07:23:12.244391Z",
     "start_time": "2024-03-28T07:22:56.822337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第1轮,耗时5.035609245300293,特征数为163,ks分别为:0.44734078810386013,0.32302927751491034,0.45606568364611255\n",
      "第2轮,耗时0.1478438377380371,特征数为51,ks分别为:0.44443613616787486,0.3287636177524311,0.46188002680965146\n",
      "第3轮,耗时0.4629850387573242,特征数为50,ks分别为:0.4435086757662621,0.32618716858681757,0.4565097184986595\n",
      "第4轮,耗时0.25037598609924316,特征数为49,ks分别为:0.4452958823325533,0.3245803695463709,0.4599467995978553\n",
      "第5轮,耗时0.9004518985748291,特征数为48,ks分别为:0.4454839417773093,0.3238805502459813,0.4669948056300268\n",
      "第6轮,耗时1.7774372100830078,特征数为47,ks分别为:0.44192782111631573,0.32548909013045885,0.46433897453083106\n",
      "第7轮,耗时0.19051241874694824,特征数为46,ks分别为:0.4468199340444077,0.32451944000529215,0.4606714979892761\n",
      "第8轮,耗时0.9849762916564941,特征数为45,ks分别为:0.4434274828660516,0.3301597746651487,0.4568888237265416\n",
      "第9轮,耗时0.17937302589416504,特征数为44,ks分别为:0.4366798072936256,0.32313024646869787,0.4655977714477212\n",
      "第10轮,耗时1.004429817199707,特征数为43,ks分别为:0.4460920862275019,0.32903867110930063,0.4632288873994638\n",
      "第11轮,耗时0.15183234214782715,特征数为42,ks分别为:0.43728121241218004,0.32618716858681757,0.46289167225201067\n",
      "第12轮,耗时0.7152655124664307,特征数为41,ks分别为:0.4420292320114402,0.33539275182179323,0.45576198056300266\n",
      "第13轮,耗时0.1916048526763916,特征数为40,ks分别为:0.44085016989534137,0.332292308602903,0.45650134048257374\n",
      "第14轮,耗时1.4841785430908203,特征数为39,ks分别为:0.4408684623669302,0.32723341584934035,0.4572825904825737\n",
      "第15轮,耗时1.173823356628418,特征数为38,ks分别为:0.4378704225496753,0.32804639001173336,0.46289167225201067\n",
      "第16轮,耗时0.18510222434997559,特征数为37,ks分别为:0.4459797640335349,0.3339374062120279,0.46477463136729225\n",
      "第17轮,耗时0.29723668098449707,特征数为36,ks分别为:0.44478112576363044,0.3313783654867226,0.46520191018766754\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[29], line 39\u001B[0m\n\u001B[1;32m     35\u001B[0m callbacks \u001B[38;5;241m=\u001B[39m [log_evaluation(period\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m), early_stopping(stopping_rounds\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m30\u001B[39m)]\n\u001B[1;32m     36\u001B[0m \u001B[38;5;66;03m#     oot_lgb = lgb.Dataset(oot[feature_select_result].values,oot['def_pd1'].values)\u001B[39;00m\n\u001B[1;32m     37\u001B[0m \n\u001B[1;32m     38\u001B[0m \u001B[38;5;66;03m# gbm = lgb.train(params,eval_lgb,num_boost_round=200,valid_sets=train_lgb,callbacks=callbacks) # 训陈数据需要参数列\u001B[39;00m\n\u001B[0;32m---> 39\u001B[0m gbm \u001B[38;5;241m=\u001B[39m \u001B[43mlgb\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_lgb\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;66;43;03m#, num_boost_round=200, valid_sets=eval_lgb, callbacks=callbacks\u001B[39;49;00m\n\u001B[1;32m     40\u001B[0m \u001B[43m                \u001B[49m\u001B[43m)\u001B[49m  \u001B[38;5;66;03m# 训陈数据需要参数列\u001B[39;00m\n\u001B[1;32m     42\u001B[0m train[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124my_p\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m gbm\u001B[38;5;241m.\u001B[39mpredict(train[feature_select_result])\n\u001B[1;32m     43\u001B[0m test[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124my_p\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m gbm\u001B[38;5;241m.\u001B[39mpredict(test[feature_select_result])\n",
      "File \u001B[0;32m~/anaconda3/envs/py311/lib/python3.11/site-packages/lightgbm/engine.py:292\u001B[0m, in \u001B[0;36mtrain\u001B[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001B[0m\n\u001B[1;32m    284\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m cb \u001B[38;5;129;01min\u001B[39;00m callbacks_before_iter:\n\u001B[1;32m    285\u001B[0m     cb(callback\u001B[38;5;241m.\u001B[39mCallbackEnv(model\u001B[38;5;241m=\u001B[39mbooster,\n\u001B[1;32m    286\u001B[0m                             params\u001B[38;5;241m=\u001B[39mparams,\n\u001B[1;32m    287\u001B[0m                             iteration\u001B[38;5;241m=\u001B[39mi,\n\u001B[1;32m    288\u001B[0m                             begin_iteration\u001B[38;5;241m=\u001B[39minit_iteration,\n\u001B[1;32m    289\u001B[0m                             end_iteration\u001B[38;5;241m=\u001B[39minit_iteration \u001B[38;5;241m+\u001B[39m num_boost_round,\n\u001B[1;32m    290\u001B[0m                             evaluation_result_list\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[0;32m--> 292\u001B[0m \u001B[43mbooster\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupdate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfobj\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfobj\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    294\u001B[0m evaluation_result_list \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m    295\u001B[0m \u001B[38;5;66;03m# check evaluation result.\u001B[39;00m\n",
      "File \u001B[0;32m~/anaconda3/envs/py311/lib/python3.11/site-packages/lightgbm/basic.py:3021\u001B[0m, in \u001B[0;36mBooster.update\u001B[0;34m(self, train_set, fobj)\u001B[0m\n\u001B[1;32m   3019\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__set_objective_to_none:\n\u001B[1;32m   3020\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m LightGBMError(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mCannot update due to null objective function.\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m-> 3021\u001B[0m _safe_call(\u001B[43m_LIB\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mLGBM_BoosterUpdateOneIter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   3022\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   3023\u001B[0m \u001B[43m    \u001B[49m\u001B[43mctypes\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbyref\u001B[49m\u001B[43m(\u001B[49m\u001B[43mis_finished\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m   3024\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__is_predicted_cur_iter \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28;01mFalse\u001B[39;00m \u001B[38;5;28;01mfor\u001B[39;00m _ \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m__num_dataset)]\n\u001B[1;32m   3025\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m is_finished\u001B[38;5;241m.\u001B[39mvalue \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "feature_select_result = model_cols_inited\n",
    "\n",
    "n = 0\n",
    "arr = []\n",
    "\n",
    "params = {\n",
    "    'task': 'train',\n",
    "    'boosting_type': 'gbdt',  # 设置提升类型\n",
    "    #     'max_bin':100,\n",
    "    'objective': 'regression',  # 目标函数\n",
    "    'metric': {'auc'},  # 评估函数\n",
    "    'max_depth': 4,\n",
    "    'num_leaves': 9,  # 叶子节点数\n",
    "    'n_estimators': 200,\n",
    "    'learning_rate': 0.05,  # 学习速率\n",
    "    #     'reg_alpha':30,\n",
    "    'min_data_in_leaf': 200,\n",
    "    'bagging_freq': 5,  # k 意味看每 k 次迭代执行bagging\n",
    "    'verbose': -1,  # <0 显示致命的，=0 显示错误 (警告)，> 显示信息\n",
    "    'colsample_bytree': 0.8  # 建树的特征选择比例  'feature_fraction': .6,\n",
    "}\n",
    "import json\n",
    "from lightgbm import log_evaluation, early_stopping\n",
    "\n",
    "train0 = pd.concat([train, test], axis=0)\n",
    "\n",
    "while len(feature_select_result) > 0:\n",
    "    n = n + 1\n",
    "    start_time = time.time()\n",
    "    train_lgb = lgb.Dataset(train[feature_select_result].values, train['def_pd1'].values)\n",
    "    # train0_lgb = lgb.Dataset(train0[feature_select_result].values, train0['def_pd1'].values)\n",
    "    eval_lgb = lgb.Dataset(test[feature_select_result].values, test['def_pd1'].values)\n",
    "    oot_lgb = lgb.Dataset(oot[feature_select_result].values, oot['def_pd1'].values)\n",
    "\n",
    "    callbacks = [log_evaluation(period=100), early_stopping(stopping_rounds=30)]\n",
    "    #     oot_lgb = lgb.Dataset(oot[feature_select_result].values,oot['def_pd1'].values)\n",
    "\n",
    "    # gbm = lgb.train(params,eval_lgb,num_boost_round=200,valid_sets=train_lgb,callbacks=callbacks) # 训陈数据需要参数列\n",
    "    gbm = lgb.train(params, train_lgb #, num_boost_round=200, valid_sets=eval_lgb, callbacks=callbacks\n",
    "                    )  # 训陈数据需要参数列\n",
    "\n",
    "    train['y_p'] = gbm.predict(train[feature_select_result])\n",
    "    test['y_p'] = gbm.predict(test[feature_select_result])\n",
    "    oot['y_p'] = gbm.predict(oot[feature_select_result])\n",
    "\n",
    "    ks1 = calc_ks(train['def_pd1'], train['y_p'])\n",
    "    ks2 = calc_ks(test['def_pd1'], test['y_p'])\n",
    "    ks3 = calc_ks(oot['def_pd1'], oot['y_p'])\n",
    "    end_time = time.time()\n",
    "\n",
    "    fea_rs = pd.DataFrame({'var': feature_select_result, 'importance': gbm.feature_importance()}) \\\n",
    "        .sort_values('importance', ascending=False)\n",
    "    fea_num_in_this = len(feature_select_result)\n",
    "\n",
    "    arr.append([n, fea_num_in_this, ks1, ks2, ks3,ks1-ks2, json.dumps(feature_select_result)])\n",
    "\n",
    "    print(f\"第{n}轮,耗时{end_time - start_time},特征数为{fea_num_in_this},ks分别为:{ks1},{ks2},{ks3}\")\n",
    "\n",
    "    import_gt_zero = fea_rs.query('importance>0').shape[0]\n",
    "    if import_gt_zero < fea_num_in_this:\n",
    "        feature_select_result = list(fea_rs[fea_rs.importance > 0]['var'])\n",
    "    else:\n",
    "        feature_select_result = fea_rs[fea_rs.importance > 0].sort_values('importance', ascending=False) \\\n",
    "                                    .reset_index(drop=True)['var'][0:-1].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_select_result = pd.DataFrame(arr,columns=['n','feature_n','ks1','ks2','ks3','delt','features'])\n",
    "feature_select_result.to_excel('fea_select240328.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于24特征进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:21:28.661133Z",
     "start_time": "2024-03-28T12:21:28.647220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['L4_r_level_app_package_ratio',\n 'loanCnt',\n 'L3_r_level_app_package_ratio',\n 'loanBillsLoanAvgAmount',\n 'L1_r_level_app_package_ratio',\n 'L2_r_level_app_package_ratio',\n 'last1EndBillsLoanAmount',\n 'financial_non_system_app_package_ratio',\n 'financial_app_package_ratio',\n 'L0_r_level_system_app_package_ratio',\n 'applyHour',\n 'non_system_app_package_ratio',\n 'L0_r_level_app_package_ratio',\n 'L0_r_level_app_package_count',\n 'endBillsCnt',\n 'wallet_sub_non_system_app_package_ratio',\n 'last1LoanBillsLoanAmount',\n 'system_app_package_count',\n 'bank_sub_app_package_ratio',\n 'lastAllAvgOverdueDays',\n 'lastEndLoanDeltaDays',\n 'creditcard_sub_app_package_ratio',\n 'refereeAffLoanPhoneMobEqAppCnt',\n 'faceBlackListMaxScore']"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fea_rs = pd.read_excel('fea_select240327.xlsx')\n",
    "feature_select_result = json.loads(fea_rs.query(\"feature_n==24\")['features'].to_list()[0])\n",
    "feature_select_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T06:47:04.407754Z",
     "start_time": "2024-03-30T06:47:04.405169Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_select_result=[\n",
    "    'L4_r_level_app_package_ratio',\n",
    "#  'loanCnt',\n",
    " 'L3_r_level_app_package_ratio',\n",
    " 'loanBillsLoanAvgAmount',\n",
    " 'L1_r_level_app_package_ratio',\n",
    " 'L2_r_level_app_package_ratio',\n",
    " 'last1EndBillsLoanAmount',\n",
    " 'financial_non_system_app_package_ratio',\n",
    " 'financial_app_package_ratio',\n",
    "#  'L0_r_level_system_app_package_ratio',\n",
    "#  'applyHour',\n",
    " 'non_system_app_package_ratio',\n",
    "#  'L0_r_level_app_package_ratio',\n",
    "#  'L0_r_level_app_package_count',\n",
    "#  'endBillsCnt',\n",
    " 'wallet_sub_non_system_app_package_ratio',\n",
    " 'last1LoanBillsLoanAmount',\n",
    " 'system_app_package_count',\n",
    " 'bank_sub_app_package_ratio',\n",
    " 'lastAllAvgOverdueDays',\n",
    " 'lastEndLoanDeltaDays',\n",
    " 'creditcard_sub_app_package_ratio',\n",
    "#  'refereeAffLoanPhoneMobEqAppCnt',\n",
    "#  'faceBlackListMaxScore'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06628051230770339\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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///xDzpw5jRf+hEhIkugI8Y5q1KhB+fLlmTFjBiEhIbi6utK3b1+8vLwYMmRIjOO3bNnC4sWLqVevHhUrVgTUlboBAwZw5coVBgwYEOPqG8Dy5cs5fvx4ov88QgghklatWrX4/fffuXHjBvXr1zfO29y1a1esx2/duhXAbHHN2MpLN2nSBAcHB+bMmWPcp2kac+fOJUeOHGYLUMf2/NiGxvn5+bF+/Xo8PDyMJa7d3NyoXbs2y5cvNxuCvWzZMgIDA/nqq6/e+POvXr2aEydO0KtXrzgVWRAivqTqmhDvoV+/fnz11VcsXryYzp07M3DgQM6cOcOkSZM4cuQIX3zxBS4uLhw8eJDly5dTpEgRlixZEuM1Ll26xNSpU9mzZw9ffvkl2bJlw9vbm40bN3L8+PEYk0+FEEJYh88++4x58+bRrl07GjduzPbt22nSpAl58uShUaNG5MuXj5cvX/LPP//w559/Uq5cORo1amR8/qBBg1iyZAm3b982FiTImTMnvXr1YvLkyYSHh1OuXDk2btzIgQMHWLFihdlyBrE9v0GDBuTMmZMKFSqQJUsW7t69y6JFi3j48CGrV682i3/cuHFUrlyZ6tWr06lTJ+7fv8/UqVOpW7cu9evXNx63f/9+Ro8eTd26dcmYMSNHjx5l0aJF1K9fn549eybeL1ikbLouVypEMvDqStbRRUZGavny5dPy5ctnXGk6MjJSW7RokValShUtbdq0mrOzs1asWDFt1KhRWmBg4GvfZ926dVrdunW1DBkyaPb29pq7u7vWrFkzbe/evYn2swkhhEg6bzqfTJkyRQO0Tz/9VFu2bJn2zTffaPny5dNcXFw0Z2dnrWjRotqQIUO0gIAAs+e1adNGA7Tbt2+b7Y+MjNTGjx+v5c6dW3N0dNSKFSumLV++PMb7xvb8WbNmaVWrVtUyZcqk2dvba5kzZ9YaNWqk7d+/P9af68CBA1rlypU1Z2dnLXPmzFq3bt1ixHnjxg2tbt26WqZMmTQnJyetcOHC2oQJE7TQ0NA4/vaEiD8bTYtlrIwQQgghhBBCJGMyIFIIIYQQQghhdSTREUIIIYQQQlgdSXSEEEIIIYQQVkcSHSGEEEIIIYTVkURHCCGEEEIIYXWSxTo6BoOBhw8fkiZNGmxsbPQORwghUgztv1XQs2fPLgv6RSPnJSGE0E9cz03JItF5+PAhHh4eeochhBAp1r1798iZM6feYVgMOS8JIYT+3nZuShaJTpo0aQD1w6RNm1bnaIQQIuUICAjAw8PD+DksFDkvCSGEfuJ6bop3orN//34mT57MqVOnePToERs2bKBp06ZvfM7evXvp3bs3ly5dwsPDg6FDh9K2bds4v2fUsIC0adPKCUUIIXQgw7PMyXlJCCH097ZzU7wHXL98+ZKSJUsye/bsOB1/+/ZtPvnkE2rWrMnZs2fp1asXHTp0YMeOHfF9ayGEEEIIIYSIk3j36DRo0IAGDRrE+fi5c+eSJ08epk6dCkCRIkU4ePAg06dPp169evF9eyGEEEIIIYR4q0Sfo3PkyBFq165ttq9evXr06tXrtc8JDQ0lNDTUuB0QEJBY4QkhhFU7cwa2blX3ly7FfNxGM+CkhRBi68rChVCpUtLHKIQQQiSGRE90vL29yZo1q9m+rFmzEhAQQHBwMC4uLjGeM2HCBEaNGhWv9zEYDISFhb1XrCL5cnR0lNK3IkUzGGDpUvD3V9vh4XDgAGze/PrnOBPMcr4lNYF8yl8EBTkkTbApSGRkJOHh4XqHIRKQg4MDdnZ2eochhIgDi6y6NmjQIHr37m3cjqqs8DphYWHcvn0bg8GQFOEJC2Rra0uePHlwdHTUOxQhkkx4OISFwT//wA8/wN27sR/XsCEULQq1aoGrq9rn4PeUooOb4HbpCAYHR47PPIXnhxWTLngrp2ka3t7e+Pn56R2KSATp0qUjW7ZsUqRDCAuX6IlOtmzZePz4sdm+x48fkzZt2lh7cwCcnJxwcnKK0+trmsajR4+ws7PDw8NDruqnQFEL9z169IhcuXLJiUdYBR8fWLYMDh9WvTWv+uOP2J+XMSPUravahQvDZ59BiRKvHHTjBrRvoO7Tp8d240ZKfyRJTkKKSnKyZMmCq6urfC5ZCU3TCAoK4smTJwC4u7vrHJEQ4k0SPdGpVKkSW7duNdu3c+dOKiXQQPCIiAiCgoLInj07rlGXKkWKkzlzZh4+fEhERAQODjL8RiQ/YWGwfTtMmACnT6vtuEqbVs2tqVEDBgyAN36nPnIEGjdWmZSnJ2zbpjIikWAiIyONSU7GjBn1DkcksKiLtE+ePCFLliwyjE0ICxbvRCcwMJAbN24Yt2/fvs3Zs2fJkCEDuXLlYtCgQTx48IClS5cC0LlzZ2bNmkX//v1p164du3fvZs2aNWzZsiVBfoDIyEgAGbKUwkX9/SMjIyXRERbNYIAZM+DOHZWQGAwwaxY4OKihaNHZ2MDo0ZApU+yvlSoVNGgA6dKBfVw+zf/6C776CkJCoGxZtf3KHMrkSI/13d4kak6OXHyzXlF/2/DwcEl0hLBg8U50Tp48Sc2aNY3bUXNp2rRpw+LFi3n06BF3ow0Uz5MnD1u2bOHHH39k5syZ5MyZk/nz5yd4aWkZFpCyyd9fWLIbN6BCBXBzg9u3Yz8mPBycnaFOHRg8GHLkgCxZII6jeOPGw0NlVLVrw6pVKlOyAlHru7Vr147PP//8rcdHre/WuXNnVqxYwa5du+jQoQPu7u4Jem6SzyXrJX9bIZKHeCc6NWrUQNO01z6+ePHiWJ9z5syZ+L6VEEIka5oGjx9DgQJq+/lz88cHDgRbW3VcrlzQpg28ZupiwihZUk36KVw4jl1AyYOs7yaEECI21nOmE0IInWka3LsHFy/Chg2wa5d5D07BgrB4MWTODPnzJ0FAQUHQoQN06wZVqqh9xYsnwRtbNlnfTQiR3PiF+PE8+PnbD4zmWdAzbvneQsO8gyLCEMGL0Be8CHtBQGgAQeFBZo9HGiIJiQghOCKY4Ihg1Q5X9+/C1qBhF6lhH6lhZ9DI/TCIcpf9oFgxOozb9k6vGVeS6OjsyJEjVK1alfr165vNW9q7dy81a9bE19eXdOnSmT3H09OTXr16mZ2U9+zZw+TJkzl27BjBwcF4enrSoEEDevfuTY4cOd4pttmzZzN58mS8vb0pWbIkP//8M+XLl3/jc/z8/BgyZAh//PEHz58/J3fu3MyYMYOGDRsC8OLFC4YNG8aGDRt48uQJpUuXZubMmZQrVy7W1+vcuTO//vor06dPf+OXECH05OcH/frB/PmvPyZNGjh7NpF7bKJ7+hQaNYJjx2DfPrh5U42NE0m2vpuIn9jObUJYk/DIcPxC/AAwaAau+lzFN8SXnTd3EhYZhvdLbyINau75o8BHnPU+S0aXjGho+Ab7xkhY3oVjBGQIBptoL+UUCZmCIPNLyBwE6ULAGXVL/98xbiFQ4w6kCgeHSLA3qJtD1H2k+Xb0fa+rh3z2agCMe+8f6Y0k0dHZggUL6NGjBwsWLODhw4dkz5493q/x66+/0rVrV9q0acP69evx9PTk7t27LF26lKlTpzJt2rR4v+bq1avp3bs3c+fOpUKFCsyYMYN69erh5eVFlixZYn1OWFgYderUIUuWLKxbt44cOXLw77//miVqHTp04OLFiyxbtozs2bOzfPlyateuzeXLl2MkZBs2bODo0aPv9DsRIrFpGkyaBCNHQrQL/YCqgla9OmTLBmPHqrk2SeraNbV4zs2bkD69mo8jSc57ie/6bsIytW3bFj8/PzZu3Kh3KMKKhUaEcurRKcIiw7jqc5V7/vdYcWEF9wPuE6lFxuu1ngU/M7ZdHVyxtYnbMip2kRplnznR6kYq8niHkONxMBn9Qkn3wjIWMI50sCfNV98m+vtIoqOjwMBAVq9ezcmTJ/H29mbx4sUMHjw4Xq9x//59fvjhB3744QemT59u3O/p6clHH330zovVTZs2jY4dO/Ldd98Bakz7li1bWLhwIQMHDoz1OQsXLuT58+ccPnzYWPnM09PT+HhwcDDr169n06ZNfPTRRwCMHDmSP//8k19++YWxY8caj33w4AE9evRgx44dfPLJJ+/0MwiREIKCYOZMNSTt5UtYulQVFfD3j3ls3rywYIEq86ybw4dV+ehnzyBPHti6VcpHvyKx13cTSS88PFwqbookFRQexLVn1wiNCOXas2t4B3pz9vFZzjw6wxWfK3F6jWyps5EzbU5ssCGNUxq+KfYNjnamKsLhhnAKZypMRpeMpHFKQ860Oc1fwGBQJ6kXL9Rn/pMnauz09OlqgmjwcyCW4W62tuoWxcFBlffMnFnd0qUzfxxUGdCyZdWkU3t7dXNwiP3+bY/Z22Nna0u+uP2q34vVJTqapv7menB1fcv6Fa9Ys2YNhQsXplChQnz77bf06tWLQYMGxauay9q1awkLC6N///6xPh7Vm3L37l2KFi36xtcaPHgwgwcPJiwsjFOnTjFo0CDjY7a2ttSuXZsjR4689vmbN2+mUqVKdOvWjU2bNpE5c2ZatGjBgAEDsLOzIyIigsjISJxfubLs4uLCwYMHjdsGg4FWrVrRr18/ihUr9rZfgRCJ4tw5WL1arWvzqleTnFmz4PPPQfe1A9evh5YtVReTFZWPTmiJvb5bbDRNizEOPqm4OsRvwdIXL17QuXNnNm7cSNq0aenfvz+bNm2iVKlSzJgxA19fX3r27Mmff/5JaGgo1atX53//+x8FoqpuAOvXr2f48OHcuHEDd3d3evToQZ8+fYyPP3nyhPbt2/PPP/+QLVs2swtdcWFjY8OcOXPYtm0bu3btol+/fgwbNoxOnTqxe/duvL29yZUrF127dqVnz56AurC2ZMkS4/NBDfuuUaMG9+7do0+fPvz999/Y2tpSrVo1Zs6caXaxTqQcweHBTDo0iVH7RmFvG/tX5QhDRJxeq1aeWhTJVITMqTKTyy0XLUq0MEtm3sjXF65ehflT4MoVCA6GwEB1e/FCXX17E1tblQx16qRWj86RA7JnhwwZ4veFNRmzukQnKAhSp9bnvQMD41etdcGCBXz7req2q1+/Pv7+/uzbt48a8bgcfP36ddKmTfvW1ZmzZ8/O2bNn33hMhgwZAPDx8SEyMjLWMexXr1597fNv3brF7t27admyJVu3buXGjRt07dqV8PBwRowYQZo0aahUqRJjxoyhSJEiZM2ald9//50jR46QP9rM7EmTJmFvb88PP/zwlp9eiIT19KkqFrBrF+zYYf5Y9+7qgldkpCpe9sEHan/evGAxy2isWaOSnEaN4PffraZ89NtY2vpusQkKDyL1BH1OToGDAknlGPd/C7179+bQoUNs3ryZrFmzMnz4cE6fPk2pUqUANfzr+vXrbN68mbRp0zJgwAAaNmzI5cuXcXBw4NSpU3z99deMHDmSZs2acfjwYbp27UrGjBmNaxW1bduWhw8fsmfPHhwcHPjhhx948uRJvH6ukSNHMnHiRGbMmIG9vT0Gg4GcOXOydu1aMmbMyOHDh+nUqRPu7u58/fXX9O3blytXrhAQEMCiRYsAdd4LDw+nXr16VKpUiQMHDmBvb8/YsWOpX78+58+fl3X6Ugi/ED+Cw4OZfHgy04+aRsi8KaHJ4JIBZ3tnUjum5oOsH1Aqayk+dP+QktlKksk1U9wTmthcvqwuWAUHv/1YGxs1TDlrVjVWOmtW1bPfvHnMnpkUxuoSneTCy8uL48ePs2HDBgDs7e1p1qwZCxYsiFeio2lanK7U2dvbmyUTicFgMJAlSxZ+++037OzsKFOmDA8ePGDy5MmMGDECgGXLltGuXTty5MiBnZ0dH374Ic2bN+fUqVMAnDp1ipkzZ3L69GlZp0AkKU1T5Z4XLjTf/+mn0L49vGX9ScuweLFasKdnTwvKvhKfpa7vlhy9ePGCJUuWsHLlSmrVqgXAokWLjHMloxKcQ4cOUblyZQBWrFiBh4cHGzdu5KuvvmLatGnUqlWLYcOGAVCwYEEuX77M5MmTadu2LdeuXWPbtm0cP37cWIhmwYIFFClSJF6xtmjRwji8Okr0ghF58uThyJEjrFmzhq+//prUqVPj4uJCaGgo2bJlMx63fPlyDAYD8+fPN553Fi1aRLp06di7dy9169aNV1zCchk0A6cfneZlmKknxC/Ej347+3H9+fUYx5fLXo5ZDWfFHDIG2NnYkSVVloT/rhIerpKc0aNNSc5HH4GjI3TpopKY1KnNb/EdUpSCWF2i4+qqelb0eu+4WrBgAREREWYT7TVNw8nJiVmzZpE2bVoA/P39Y1Rd8/Pzw83NDVAnEH9/fx49evTGXp34DF3LlCkTdnZ2sY5hj35yeJW7uzsODg5mq0QXKVIEb29vwsLCcHR0JF++fOzbt4+XL18SEBCAu7s7zZo1I2/evAAcOHCAJ0+ekCtXLuNrREZG0qdPH2bMmMGdO3fe+DMIEVcREarX5vlzleS0aWN6zM1NjQLLnTuJykC/q6AgVeqtRw91knNxgWgT5lOK5LC+m6uDK4GD9Dk5uTrE/eR069YtwsPDzSpsurm5UahQIQCuXLmCvb09FSpUMD6eMWNGChUqxJUrV4zHNGnSxOx1q1SpwowZM4iMjDS+RpkyZYyPFy5cOMa57m3Kli0bY9/s2bNZuHAhd+/eJTg4mLCwMGNP1OucO3eOGzdukCZNGrP9ISEh3Lx5M14xCcty/dl1fIJ8+OXkL9jb2nP60WnOPT73xuc42jnSvnR7JtWeRBqnNG889r2Ehqpxz9eugZcXhISo25UrEBZmOm7CBHUVTrwTq0t0bGwsf7RGRESEsSLaq1eKmjZtyu+//07Lli2xtbXl1KlT5M6d2/j4rVu38Pf3p2DBggB8+eWXDBw4kJ9++smsGEEUPz8/0qVLF6+ha46OjpQpU4Zdu3bR9L/L2AaDgV27dtG9e/fXPr9KlSqsXLkSg8GA7X9dpdeuXcPd3T1G13+qVKlIlSoVvr6+7Nixg59++gmAVq1axbq+RatWrWJcuRPiXRw9Cj//DCtXvv6YrVvhv4vVluvJEzU04dgx8PFRV/+ExbKxsYnX8DHxdqleOdmvWrWKvn37MnXqVCpVqkSaNGmMyy68SWBgIGXKlGHFihUxHsucOXOCxiwSX1hkGOP2j+P3i7/H2kuT2jE1HmlNFROd7J34IOsHVMtVjQ4fdkjc4Hx81LCBa9dU5ZrXcXOD0qXV5M83fO8Sb2d1iU5y8Ndff+Hr60v79u2NPTNRvvjiCxYsWEDnzp3p0KEDffr0wd7enhIlSnDv3j0GDBhAxYoVjUMGPDw8mD59Ot27dycgIIDWrVvj6enJ/fv3Wbp0KalTp2bq1KnxHrrWu3dv2rRpQ9myZSlfvjwzZszg5cuXZslG69atyZEjBxP+m63dpUsXZs2aRc+ePenRowfXr19n/PjxZnNtduzYgaZpFCpUiBs3btCvXz8KFy5sfN2MGTOSMWNGs1gcHBzIli2b8YqiEO/CYFDloGMrbFivnurVKVAA/ve/ZDCk+do1aNAAbt1Sk0plaI1IIHnz5sXBwYETJ04Ye9b9/f25du0aH330EUWKFCEiIoJjx44Zz0PPnj3Dy8vLOGqgSJEiHDp0yOx1Dx06RMGCBbGzs6Nw4cJERERw6tQp49A1Ly+vd64SGv09KleuTNeuXY37Xu2RcXR0JDLSvLzvhx9+yOrVq8mSJYtxNIVIntZfXk+nvzqZLayZI00OIgwRFMxYkGbFmvFN8W/I6JrxDa+SgA4eVFfXQJ1kRoyIOecmf361P21aNeS4cGE1+VOGoiUMLRnw9/fXAM3f3z/GY8HBwdrly5e14OBgHSJ7N59++qnWsGHDWB87duyYBmjnzp3TgoODtREjRmiFCxfWXFxctDx58midOnXSnj59GuN5O3fu1OrVq6elT59ec3Z21goXLqz17dtXe/jw4TvH+fPPP2u5cuXSHB0dtfLly2tHjx41e7x69epamzZtzPYdPnxYq1Chgubk5KTlzZtXGzdunBYREWF8fPXq1VrevHk1R0dHLVu2bFq3bt00Pz+/N8aRO3dubfr06W88Jjn+OxBJp3FjTVNnGdOtUydNmzdP054/1zu6eDp4UNMyZFA/RJ48mnb1aqK+3Zs+f1MyazsvRdehQwctT5482u7du7WLFy9qX3zxhZYmTRqtV69emqZpWpMmTbSiRYtqBw4c0M6ePavVr19fy58/vxYWFqZpmqadOnVKs7W11UaPHq15eXlpixcv1lxcXLRFixYZ36N+/fpa6dKltaNHj2onT57Uqlatqrm4uLz1sz4KoG3YsMFs38yZM7W0adNq27dv17y8vLShQ4dqadOm1UqWLGk8Zty4cVquXLm0q1evak+fPtXCwsK0ly9fagUKFNBq1Kih7d+/X7t165a2Z88erUePHtq9e/diff/k/je2BmERYdrjwMfajCMztIkHJmqMxHhLNS6VVndZXe3py5jflxKdwaBp//ufpuXLF/PEE3X78ktNW7xY0+7cSfr4rERcz02S6AirIP8OxKvu3NG0MWM0rXv3mOeYrVv1ju4drV2raU5O6ocoV07TvL0T/S0l0YmdNZ+XAgICtBYtWmiurq5atmzZtGnTpmnly5fXBg4cqGmapj1//lxr1aqV5ubmprm4uGj16tXTrl27ZvYa69at04oWLao5ODhouXLl0iZPnmz2+KNHj7RPPvlEc3Jy0nLlyqUtXbo0The1osSW6ISEhGht27bV3NzctHTp0mldunTRBg4caJboPHnyRKtTp46WOnVqDdD27NljjKd169ZapkyZjBfqOnbs+Np/98n9b5ychUWEaX139DVLbF69PQh4kLRBnTunaVOnatpPP2lanTqmk42trfqsbt1a3dq00bTNm5M2NisV13OTjaa9YQanhQgICMDNzQ1/f/8Y3cohISHcvn2bPHnyxFifRaQc8u9ARHny5PVLx9y5owoMJEsPH6rhDKGham7OypVJMiHxTZ+/KVlKOi+9fPmSHDlyMHXqVNq3b693OBbB2v7GycH1Z9fpvKUzu2/vjvHYJwU+IXOqzASGBTK/0XzcnN1ieYVEMmcOdOsWc3+XLqqQgFsSxpKCxPXcJHN0hBBW4+BBqFbNfF+qVGqttN69IWfMCqHJR/bsahLrkSMwY0aKKh8tktaZM2e4evUq5cuXx9/fn9H/Fbp4tZKaEEllwM4B/HT4pxj7L3a5SKFMhV67qGeievIEbtyAAQNM+6LKd2bJAqNGqUqYQleS6AghrMK5c+ZJztChqhBZsp7PGRQE3t6qJwegRQt1EyKRTZkyBS8vL2MVzgMHDpApU6Ykee8VK1bw/fffx/pY7ty5uXTpUpLEIfT36MUjev/dm1UXVxn3jft4HM2LNydP+jz6BBUQoK6erV5tvv/IEahYUZ+YxGtJoiOESPY+/xz+W3sXgMOHoVIl/eJJEE+eQKNG8PixOoG+YZ0sIRJS6dKljYs466Fx48Zm6/RE5+DgkMTRCL386/cvnjM9jdt18tZh7Vdrk3ZYWnTh4TBypFqf4MULtS9rVrUWwdCh8OGH+sQl3kgSHSFEshQZCadPQ4cOcP68af+UKVaQ5Hh5qfLRt2+r8tH370uiI1KMNGnSxFi8U6Q8Uw5PMbXrTKFP5T5JH8SpU/DHH3DvHmzZolaYBsiVS/XoSA+OxbOaRCcZ1FQQiUj+/imHpqm5n6+uoZY+vSo2kOznyx88CE2aqBNq3rywbRv8t0CwEEJYs0hDJD/u+JGDdw9yxvsMAN3LddcnyTl4ED7+WPXkRHFxgdmz1Vwci19wTYAVJDoODg7Y2Njw9OlTMmfOjE2yHpAv3oWmaTx9+hQbGxsZ1mDFNE1dQJs507T+WpTJk6FXL7BP7p9oa9dCq1aqslqFCrB5s5rUKoQQViosMozxB8YTFhnGlMNTCDeYEovquaszo/6MpA3o/n01F/LAAbWdJw98/73qxalWLZlXtUl5kvvXAuzs7MiZMyf379/nzp07eocjdGJjY0POnDmxk0pUVunUKdWD82qCs28ffPSRPjEluDVroFkz1W7SRJWPdnXVNyYhhEgkz4Ke8fPxnxm1b1SMx2p61mTsx2Mpn6M8drZJeF5//Bg8PEzbDg7qglPx4kkXg0hQyT7RAUidOjUFChQgPHr3okhRHBwcJMmxQi9ewJ9/QsuW5vvXrIGGDZNkGZmkU6cOFC0KtWrB9OlSPloIYZX23N7DygsrmX9mfozHOpfpTNPCTambr27SjtDx8lInlv/9z7SvWTNVulOGDidrVpHogOrZkS+6QiRv16+rpWImToz98dq14aefoHTppI0r0YSFgaOjaqdPr6qrpUmTzGtiCyFE7KYenkrfnX3N9jUu1JgxNcfwQdYP9Alq9uyYkz6bN1e96iLZs5pERwiRvH37LaxYEXN/liyqilrv3lY0TA3UEIlGjdScnB491L5kX0lBJHc1atSgVKlSzJgxI8Fec+/evdSsWRNfX1/SpUuXYK8rko/g8GAKzy7MXf+7xn3LP1tOzTw1yZ4muz5BhYZC9epw7JjazpgRBg9WZaKrV9cnJpHgJNERQuhu7lzzJOfjj1WvTu7ckCmTFRa3iV4++t9/oXVrcNNpbQghrISNjQ0bNmygadOmeocigPDIcJafX86eO3tYf2U9QeFBAKRzTsfjvo9xtHPUN8CRI01JDqiynalT6xWNSCSS6AghklxAADx7poapbdwIFy+aHgsNNY3mskoHDqhiA76+kC+fKh8tSY4QsYqMjMTGxgZbq7vaYb0MmoHZx2fzw/YfYjzWq0IvptSdkrQFBqILDFRVbWbNgk2b1L5OndTVNhkybJXkk0MIkeg2bIBixdQQtIIF1ff6vHlh7FjzJOfcOStPclavVhONfH1V+egjR6BAAb2jEklB0+DlS31u8VxnLCIigu7du+Pm5kamTJkYNmyYca2yZcuWUbZsWdKkSUO2bNlo0aIFT548MXv+1q1bKViwIC4uLtSsWTNeFVEXL15MunTp2Lx5M0WLFsXJyYm7d+9y4sQJ6tSpQ6ZMmXBzc6N69eqcPn3a+DxPT08APvvsM2xsbIzbAJs2beLDDz/E2dmZvHnzMmrUKCIiIuL1OxFvF2GIYOA/Ayk8q7AxybG1saV23tos/2w5IUNCmF5/uj5JzuPHMHCgmgNZp44pyfnxR0lyrJz06AghEtXo0TBiRMz9rq7q3JIjh5rz+eGHVn6umToV+v43Cfezz2D5cikfnZIEBek3LCYwMF4lCpcsWUL79u05fvw4J0+epFOnTuTKlYuOHTsSHh7OmDFjKFSoEE+ePKF37960bduWrVu3AnDv3j0+//xzunXrRqdOnTh58iR9+sRvscegoCAmTZrE/PnzyZgxI1myZOHWrVu0adOGn3/+GU3TmDp1Kg0bNuT69eukSZOGEydOkCVLFhYtWkT9+vWNxYkOHDhA69at+d///ke1atW4efMmnTp1AmBEbB9M4p28DHtJmd/K4PXMCwBXB1fG1BxDxw87ksYpTdIHZDCo3vKDB+HGDdiyBYKDTY+3awf9+kHhwkkfm0haWjLg7++vAZq/v7/eoQgh4uHFC01Tl5PVrUcPTdu5U9OePNE7Mh1Mm6Z+CT/8oGkREXpHE2fy+Ru7N/1egoODtcuXL2vBwcGmnYGB5v8ZkvIWGBjnn6t69epakSJFNIPBYNw3YMAArUiRIrEef+LECQ3QXrx4oWmapg0aNEgrWrSo2TEDBgzQAM3X1/et779o0SIN0M6ePfvG4yIjI7U0adJof/75p3EfoG3YsMHsuFq1amnjx48327ds2TLN3d39rbG8Sax/4xTozKMzGiMxu7Vc31K7739f38Dy5Yv5/6B0aU1bvlzT4vDvUFi+uJ6bpEdHCJHg/PxUT86NG6Z9VrW457vo1QtKlYKaNfWOROjB1VX1rOj13vFQsWJFszVMKlWqxNSpU4mMjOTs2bOMHDmSc+fO4evri8FgAODu3bsULVqUK1euUKFCBbPXq1SpUrze39HRkQ8+MC81/PjxY4YOHcrevXt58uQJkZGRBAUFcffu3de8inLu3DkOHTrEuHHjjPsiIyMJCQkhKCgIV+lVfWcXHl+g9K/mtf6/K/UdCxovSNo1cF41ejTcvGnaHjBAVbj5+GOwl6+9KY38xYUQCcbPT1Xo/O+7j1HZsikwyfH2VifY//1PTUqysZEkJyWzsUn2K9yGhIRQr1496tWrx4oVK8icOTN3796lXr16hIWFJdj7uLi4xPii3KZNG549e8bMmTPJnTs3Tk5OVKpU6a3vGxgYyKhRo/j8889jPObs7JxgMackBs1A1YVVOXL/iHHf2JpjGVh1oH5FBqJ07Ajzoy1EGhlphWU7RXxIoiOESDAtW5onOV9+Cd26qSIEKcrVq6p89J07EBKiihAIkUwci15yFzh69CgFChTg6tWrPHv2jIkTJ+Lh4QHAyZMnzY4tUqQImzdvjvH893Xo0CHmzJlDw4YNATUXyMfHx+wYBwcHIiMjzfZ9+OGHeHl5kT9//veOQagkp8+OPsYkJ5NrJvpV7kf/Kv11jgw4fdo8yXn0SJIcIYmOEOLdXLsGW7eqC2a3bqky0Q8fmh6/f18VGkhx9u+Hpk1VZbX8+SHakBkhkoO7d+/Su3dvvv/+e06fPs3PP//M1KlTyZUrF46Ojvz888907tyZixcvMmbMGLPndu7cmalTp9KvXz86dOjAqVOnWLx48XvHVKBAAWPFt4CAAPr164eLi4vZMZ6enuzatYsqVarg5ORE+vTpGT58OJ9++im5cuXiyy+/xNbWlnPnznHx4kXGjh373nGlJKcenqLsvLLG7Y9yf8TeNnv1HaYWRdMgquhFnTrq5CTD1ARSXloIEU937sDQoVCokKrM2bcvzJljnuTcvJlCk5xVq9RJ1tcXKlaEw4dVsiNEMtK6dWuCg4MpX7483bp1o2fPnnTq1InMmTOzePFi1q5dS9GiRZk4cSJTpkwxe26uXLlYv349GzdupGTJksydO5fx48e/d0wLFizA19eXDz/8kFatWvHDDz+QJUsWs2OmTp3Kzp078fDwoHRpNXekXr16/PXXX/z999+UK1eOihUrMn36dHLnzv3eMaUkf3r9aZbk9KzQkz1t9lhGkhMcDIMHw969an2CKVMkyRFGNpoWzwL7OggICMDNzQ1/f3/Spk2rdzhCpDgTJ6o11m7cgEuXzB/79lt1//KlmofTsKFaKydF0TSYPFnNyQH4/HNVPvqVK87JkXz+xu5Nv5eQkBBu375Nnjx5ZB6IlUpJf+P1l9fz5dovjdvrvlrHF0W/0DGiaBYsUJ+7z56p7f79YdIkfWMSSSKu5yZJeYUQMfj6QtQw/SNHVBGbVzVtCt9/D/XrJ2lolsnPD37+WbV79VJXFO10npQrhBDvqfri6uz/dz+gFv982PshWVNn1Tmq/6xaBR06qHbWrDBqFLRvr29MwuJIoiOEMLp1C776Ss3pjM1vv6nv7598os4r4j/p06sx4fv2QffuekcjhMVq0KABBw4ciPWxwYMHM3jw4CSOSLzOH1f+MCY5hTMVZn/b/WROlVnnqP6zcqWqfgOqouG//4KTk74xCYskiY4QKVhYmBqOZjCo4c09epg/nj8/pE0LuXKpYjYZM+oSpmXy9oYzZ1R1NYASJdRNCPFa8+fPJzj6CvXRZMiQIYmjEa8TGhFK5786A5DZNTOXu162jPk4oCaKtmql2jVqwPr1kuSI15JER4gU6p9/oEULePo05mOzZkHr1pAmTdLHlSxcuaISnEeP1C+yWjW9IxIiWciRIquUJC+Rhki+WvsVT4Oekt45PV7dvSwnyQHVm2MwQJky6vNXhgmLN5BER4gUJDRU9dzMnavKQUfJkkUVqaleHcaPB09PnQJMDvbtUxOU/PygQAFwd9c7IiGESBCPAx+TbWo24/agqoNI75Jex4heERkJ8+apdvfukuSIt5JER4gUYvbsmNNHPDxgyxYZcRVnK1fCd9+pMX+VKsHmzZApk95RCSHEewsODzZLcgA6lumoUzSvMXq0GrqWIQM0a6Z3NCIZkHV0hLByEydCrVrmSU6PHnD5Mty9K0lOnGia+kW2bKmSnC++gF27JMkRQliFZ0HPqLOsjnF7UNVBGIYbSOecTr+gXrVypakE6PTpVlG+XyQ+6dERwsqEhKilBVavhtiKG/39t1rTUsTDxo0waJBq9+6t1syxletEQgjrUHROUZ68fALAV0W/Ynyt91/kNUFdvmyqspYnj5pEKkQcSKIjhBXRNMieXa2DE1327OpiWKVKauFoEU9NmqjKDRUqwA8/6B2NEEIkGE3TeBH6AoBGBRsxvd50nSOKJiAAhgxRFXKi/P23fvGIZEcuSQqRzPn6wrhxal58zpymJKd0aRg5Es6dgwcPVKEBSXLi4fFj1T0Gqvdm+XJJcoRIRJ6ensyYMeO1j9+5cwcbGxvOnj2b4O/dtm1bmjZtmuCvmxw8CnxEcIQq+b36y9XkSGshlfECAtSJLXqSs3y5WvdAiDiSHh0hkiGDAf74Q13YWrQIIiLMH2/SRC0a7eysT3zJ3uXLqnx0pUqqK8zWVi1KJ4QQVmbq4akAlM1eFhcHC5r3snYtPFHD6bCxgVOn1BU8IeJBEh0hkhF/f+jWDQ4eVAtBR3F0VFU3ly+HqlVVz454R3v3qvLR/v5qETofH1V/WwghrEykIZLZJ2YD8LHnxzpHE82TJ9Chg2q3aQOLF+sajki+ZOiaEMnIjz/CihXmSc4//6gRVhER8M03kuS8l5UroW5dleRUqQJHjkiSIxKEpsHLl/rcNC1+sb548YKWLVuSKlUq3N3dmT59OjVq1KBXr14A+Pr60rp1a9KnT4+rqysNGjTg+vXrZq+xfv16ihUrhpOTE56enkydOtXs8SdPntCoUSNcXFzIkycPK1asiHN8V69epXLlyjg7O1O8eHH27dtnfCwyMpL27duTJ08eXFxcKFSoEDNnzjR7fmRkJL179yZdunRkzJiR/v37o8X3l2Ql6i2vR2hkKAADqg7QOZr/3LwJhQqZths10i8WkexJoiNEMnHjhhqmFmX9erUAaK1aMqrqvWkaTJigqvqEh8NXX6kMMmNGvSMTViIoCFKn1ucWFBS/WHv37s2hQ4fYvHkzO3fu5MCBA5w+fdr4eNu2bTl58iSbN2/myJEjaJpGw4YNCQ8PB+DUqVN8/fXXfPPNN1y4cIGRI0cybNgwFke7Kt+2bVvu3bvHnj17WLduHXPmzOFJ1DClt+jXrx99+vThzJkzVKpUiUaNGvHs2TMADAYDOXPmZO3atVy+fJnhw4czePBg1qxZY3z+1KlTWbx4MQsXLuTgwYM8f/6cDRs2xO+XZAVOPDjBrtu7AFjYeCEZXDLoHBFw9aqag+Pnp7YXLVLl/IV4V1oy4O/vrwGav7+/3qEIkeTu39e0FSs0rVAhTVPfyDVt61a9o7Iy/fqZfrl9+2paZKTeEVkM+fyN3Zt+L8HBwdrly5e14OBg477AQNM/saS+BQbG/ecKCAjQHBwctLVr1xr3+fn5aa6urlrPnj21a9euaYB26NAh4+M+Pj6ai4uLtmbNGk3TNK1FixZanTp1zF63X79+WtGiRTVN0zQvLy8N0I4fP258/MqVKxqgTZ8+/bWx3b59WwO0iRMnGveFh4drOXPm1CZNmvTa53Xr1k374osvjNvu7u7aTz/9FOM1mjRp8trXeFVsf+PkpsnvTTRGomWclFHvUJTISE3Lk8f0D/fCBb0jEhYsruemd+rRmT17Np6enjg7O1OhQgWOHz/+xuNnzJhBoUKFcHFxwcPDgx9//JGQqGpGQojXOnpUXdxq2RK8vNSc+L//VvPkRQJq1EgtPvfzz7JGjkgUrq4QGKjPzdU17nHeunWL8PBwypcvb9zn5uZGof+GEl25cgV7e3sqVKhgfDxjxowUKlSIK1euGI+pUqWK2etWqVKF69evExkZaXyNMmXKGB8vXLgw6dKlM2537tyZ1KlTG2/RVapUydi2t7enbNmyxvcG9R2lTJkyZM6cmdSpU/Pbb79x9+5dAPz9/Xn06JFZ/FGvkZJ4+XixyWsTAL9/8bvO0fzn99/h9m3VnjMHihfXNx5hFeJdjGD16tX07t2buXPnUqFCBWbMmEG9evXw8vIiSyxj2VeuXMnAgQNZuHAhlStX5tq1a7Rt2xYbGxumTZuWID+EENbo/n1V9AvU9+4hQ9TczFy59I3LahgMpoSmWjW4dQuyZdM3JmG1bGwgVSq9o0g+Ro8eTd++feP9vFWrVtG3b1+mTp1KpUqVSJMmDZMnT+bYsWOJEGXy8yL0BYN3DWbWCVWyOZ1zOurks4AVpP/6C779VrWrV4cuXfSNR1iNeF+2nDZtGh07duS7776jaNGizJ07F1dXVxYuXBjr8YcPH6ZKlSq0aNECT09P6tatS/Pmzd/YCxQaGkpAQIDZTYiURNOgXTvT9rNnMHq0JDkJ5tIlKFUKLlww7ZMkRwjy5s2Lg4MDJ06cMO7z9/fn2rVrABQpUoSIiAizxOHZs2d4eXlRtGhR4zGHDh0ye91Dhw5RsGBB7OzsKFy4MBEREZw6dcr4uJeXF35R8zKALFmykD9/fuMtuqNHjxrbUa9TpEgR4/tUrlyZrl27Urp0afLnz8/NmzeNx7u5ueHu7m4W/6uxWLM8M/MYkxxne2fWfLnmLc9IAj4+0KyZahcvrnrWhUgg8Up0wsLCOHXqFLVr1za9gK0ttWvX5siRI7E+p3Llypw6dcqY2Ny6dYutW7fSsGHD177PhAkTcHNzM948PDziE6YQyV7TprBzp2rXqAHRRnSI97V7t6qoduGCKmMnhDBKkyYNbdq0oV+/fuzZs4dLly7Rvn17bG1tsbGxoUCBAjRp0oSOHTty8OBBzp07x7fffkuOHDlo0qQJAH369GHXrl2MGTOGa9eusWTJEmbNmmXsoSlUqBD169fn+++/59ixY5w6dYoOHTrg4hK3NVxmz57Nhg0buHr1Kt26dcPX15d2/10ZKlCgACdPnmTHjh1cu3aNYcOGmSVtAD179mTixIls3LiRq1ev0rVrV7Mky1r9cuIXngWrog1T607Fu4+3ZfTmbNqkKmbkzQsnT0KJEnpHJKxJfCb+PHjwQAO0w4cPm+3v16+fVr58+dc+b+bMmZqDg4Nmb2+vAVrnzp3f+D4hISGav7+/8Xbv3j2ZDCus2q1bmta2raYVLappdnamuZilS2va8+d6R2dFli3TNAcH9cutWlXTfHz0jsjiJadiBLNmzdJy586tOTk5aeXLl9eOHTv2xuOnT5+uFSxYUHN2dtZy5syp9erVK86Ty+NbjCA5CQgI0Fq0aKG5urpq2bJl06ZNm6aVL19eGzhwoKZpmvb8+XOtVatWmpubm+bi4qLVq1dPu3btmtlrrFu3TitatKjm4OCg5cqVS5s8ebLZ448ePdI++eQTzcnJScuVK5e2dOlSLXfu3HEqRrBy5UqtfPnymqOjo1a0aFFt9+7dxmNCQkK0tm3bam5ublq6dOm0Ll26aAMHDtRKlixpPCY8PFzr2bOnljZtWi1dunRa7969tdatW1ttMYKwiDCt9NzSGiPRGIlWfE5xvUNSIiM17cgRTbO1VZ/JAwboHZFIRuJ6bkr0RGfPnj1a1qxZtXnz5mnnz5/X/vjjD83Dw0MbPXp0nN83OZ1ohYiv7dtjr5SUM6emhYToHZ2VMBg0bexY0y/36681LRl8QbEEyeXzd9WqVZqjo6O2cOFC7dKlS1rHjh21dOnSaY8fP471+BUrVmhOTk7aihUrtNu3b2s7duzQ3N3dtR9//DFO72fNic6rAgMDNTc3N23+/Pl6h2IxktPfeOy+scYkh5Fo159d1zskZeRI85OeXNUT8RDXc1O8ihFkypQJOzs7Hj9+bLb/8ePHZHvN+PZhw4bRqlUrOvy3wm2JEiV4+fIlnTp1YsiQIdhKdSORQvn4wKefQvQ5srlzw9SpULUqZMgADg76xWc1wsOha1eYP19t9+0LkyZJZTUrE33+KMDcuXPZsmULCxcuZODAgTGOjz5/FMDT05PmzZvLpHXgzJkzXL16lfLly+Pv78/o0aMBjEPTRPIRHhnO0D1DjdsRwyKws7XTMSJUWjNxIowcqbazZIFffoH06XUNS1ineJ3pHR0dKVOmDLt27TLuMxgM7Nq1y6zcY3RBQUExkhk7O/WfTEuhKxELoWmwZo15krNsGdy5o9ZGy5pVkpwEo2mqZKmtLcyaJeWjrVBSzB9NaUVypkyZQsmSJalduzYvX77kwIEDZMqUSe+wRDxomsakQ5OM2+c7n9c/yQG4eBEGD1btggXB2xs+/1zfmITVind56d69e9OmTRvKli1L+fLlmTFjBi9fvjReRWvdujU5cuRgwoQJADRq1Ihp06ZRunRpKlSowI0bNxg2bBiNGjUyJjxCpBSRkSqRuXgRogoBubjA8+fg7KxvbFbL0RHWr1dZZd26ekcjEoGPjw+RkZFkzZrVbH/WrFm5evVqrM9p0aIFPj4+VK1aFU3TiIiIoHPnzgyO+gL2igkTJjBq1KgEj90SlS5dOsVUIbNW9wPu02BFAy4+uQhA7by1KZHVAib5//MP1IlWAGHfPlV7XYhEEu9Ep1mzZjx9+pThw4fj7e1NqVKl2L59u/EEc/fuXbMenKFDh2JjY8PQoUN58OABmTNnplGjRowbNy7hfgohkoHISMiTB+7dM+1Lk0aVjZYkJ4FdvAgbNsDQoeok6uYmSY4ws3fvXsaPH8+cOXOMF+F69uzJmDFjGDZsWIzjBw0aRO/evY3bAQEBUhFUWKzpR6Ybkxx7W3tm1p+pc0TAhAmmnhxQPexS1l8kMhstGYwfCwgIwM3NDX9/f9KmTat3OELES2Sk+jzv1ct8/+PHamiySGC7d8Nnn0FAgJqX07693hEla8nh8zcsLAxXV1fWrVtH06ZNjfvbtGmDn58fmzZtivGcatWqUbFiRSZPnmzct3z5cjp16kRgYOBb54++6fcSEhLC7du38fT0jHPJZJG8BAcHc+fOHfLkyYOzhV2pOv3oNGV+KwNAm5JtWNx0sb4BaRo0bqwWBY3y22/QsaN+MYlkL67nJhmoLkQi8fKCP/4Ae3vzJMfJCcLCJMlJFMuWQf36KsmpVk0lPMLqWdr8UYf/JtgFBQW91+sIyxX1t3WwsMmUmqZReUFlAFwdXPn10191jgi1bln0JCcwUJIckWTiPXRNCPFmL1/C4sXQvbv5/s6d4bvvoHx5XcKybpoG48ZB1JCjb76BRYtkTGAKYknzR+3s7EiXLh1PnjwBwNXVFRuZh2AVNE0jKCiIJ0+ekC5dOouba3zT9yahkaEArP1qLU72TvoG9NtvEL0gSHi4uvonRBKRf21CJCCDQS3qfPu2aV/u3KqgzLRp+sVl1cLDoUsXWLBAbQ8YAOPHS2W1FMbS5o9GLbkQlewI65IuXbrXLquhp+//+h4AOxs7GhaIvYJgktm61XTF75NP4M8/pfCASHIyR0eIBPL0Kbi7qzk5oBKc+fMhWsVbkRgOHIDq1dUJdNYslfSIBCOfv7GL6+8lMjKS8PDwJIxMJDYHBweL68kBOPHgBOXnqyED4z8ez6Bqg/QLZtw4VQwGVBWeixfB1VW/eITVietnsPToCJFABg82JTmdO6v1z0QSqFYNZs+GnDmhUSO9oxHCjJ2dnUV+KRbW5fiD41SYX8G43f5DHYuw/P23KclJlQoOH5YkR+hGEh0h3pOmwahRqvcGIEcO+N//9I3J6l24AGnTqm4zkF4cIUSKZdAMtN3Y1rh98LuDZEmlU7WbBg1g+3bT9uPHKtkRQicyiF2I9zRhgkp0AAoXhhs3wMIK8ViXXbugalV1QvX11TsaIYTQ1eRDk7nicwWAEx1PUCVXFX0CWbzYlORkzgzLl0uSI3QnPTpCvANNg8mT4dQpWLPGtP/CBSkok6iWLIEOHSAiAkqVUn8IIYRIoXyDfRm4ayAADQs0pGz2svoF81+FQ0D15EjhAWEB5CuZEHGkaXD1qvosP3Ys5uMnTkiSk2g0DcaMgREj1PY336irh046l04VQgidaJpG5smZjdtLmy7VL5iLF03ty5clyREWQ4auCfEWmgbe3moaSNGi5kmOvT0MHAhnz0JZHS+kWbXwcGjf3pTkDBgAK1ZIkiOESNHmn55PpKYq4GxpsYWMrhn1CSQ0FPr0Ue2PPoIiRfSJQ4hYyPVnIV7j0iX45x/1+R1VTS1Kly7QsycUKqRPbClK//5q8U9bW1VdrXNnvSMSQghdRRgi6PRXJwByps2p75o5Y8eqSmu2tqothAWRREeIWEREQPHi5vscHSFTJjXXskQJfeJKkfr3VyfRSZPg00/1jkYIIXR34sEJY3vtV2v1C+TZM1NyM2yYKvcvhAWRREeIVzx4oJZkiVK/PnTsCJ9/rl9MKc6zZ5Dxv2EY7u5w/jzIWiRCCIGmabTZ2AaA/BnyUzFnRf2CWRstyerbV784hHgNmaMjxCuWLDG1y5eHbdskyUlS//wD+fLBypWmfZLkCCEEAD8f/5nrz68DMKL6CH2DiZqb89VXkDq1vrEIEQtJdISIxtcXNm5U7XLl4OhRXcNJeRYvVuvj+PurtpSPFkIIo4cvHtJze0/j9rcffKtfMJs3Q1CQag8dql8cQryBJDpCAAaDKuyVIYMqEw1qWypkJhFNU6uufvedmiDVvDn8+af8AYQQIppRe0cZ25e6XtIvkM6doUkT1W7SBD74QL9YhHgDmaMjUjyDAfLkgbt3TfvKlpXhakkmPBw6dVI9OACDBqnJrbZyHUYIIaI77X0agIWNF1I0c1H9Alm2TN2XLm0+zFgICyOJjkjR1q1TQ4uj8/KCggX1iSfFCQ+HTz6BnTtVYjNnDnz/vd5RCSGExbn27BonH57EBhtq5qmpXyAXLpiGrO3aBa6u+sUixFvIJVORYt28GTPJuXtXkpwk5eCgJkOlSqWGqkmSI4QQser4Z0cAquaqimc6T/0C+fNPdV+mDKRPr18cQsSBJDoixcqf39Reu1Yt7uzhoV88KdbYsXDuHDTUccE7IYSwYJu9NrP/3/0ANCrYSN9gZs9W92nS6BuHEHEgiY5IUW7dgoED1YWoKF26wJdfqgVBRRLYuVNVVgsOVts2NqqctBBCiFj1+buPsf1jpR/1C+T6dXj4ULWbNtUvDiHiSOboiBSlYEGIjDTfN2eOPrGkSIsWqcIDEREwZYpaSVsIIcRr3Q+4z43nNwBY99U67G11/Oo2fry6z54dunbVLw4h4kh6dESKsX69KcnJmhV27ICQEH1jSjE0DUaOhHbtVJLTogX07693VEIIYfFWnF8BgIu9C58X0bEc6PPnpuqYM2eqOZZCWDjp0RFW7coV6NdPFYjZs8e0/9IlyJhRv7hSlLAw1YuzZInaHjIExoyRNXKEEOItngU9Y+CugQBMqDUBGz0/N3fuNLU/+US/OISIB0l0hFX68081SmrDhpiPHTsmSU6S8feHL75QJUjt7NQ4wU6d9I5KCCGShTWX1hjbbUu11S+Qhw/hm29Uu3lzcHHRLxYh4kESHWFV7t5Vi38aDOb7p01TQ4qLFoUSJfSJLUV6+hTOnlXlo9euVUUIhBBCxMn5x+cBKJe9HG7ObvoF8t136t7OTvXKC5FMSKIjrMKLF7BwIfTqZb5/7lxVYa1sWV3CEvnzq+41Jyf48EO9oxFCiGQjKDyIRWcXAfBJAR2Hii1fDn//rdq//ALFiukXixDxJImOSPamTYM+fcz3ffopbNoEtlJuI+lFnRDr1lX3lSrpF4sQQiRTPx/7mdDIUAD6VO7zlqMTyfPn0KqVajdoAB076hOHEO9IvgaKZMvXV3UWvJrkzJwpSY5uFi5UC39+8QVcvap3NEIIkSytvLDSWIQgg0sGUjum1ieQGjVM7UmT9IlBiPcgPToi2bl+Xa1Tdvmy+f5Tp2R0lG40DUaMUNXUQP2B8ubVNSQhhEiOum3pxpyTpgXeDrc7rE8gwcFw4YJq16ghE1xFsiTXvEWycecOlC+vFv18NcmZN0+SHN2EhUGbNqYkZ8gQWLoUHB31jUsIIZKZZ0HPzJIcr+5eFMpUSJ9gvv9e3adKBVu36hODEO9JenREsmAwqGpq0Q0dCqNHy3IsuvL3h88/h927VTWeX36RMdxCCPGOphyeYmz7D/QnrVNa/YL54w91X7KklJMWyZYkOsLinTmjRkJFqVZNff5myqRbSCLKjBkqyUmdGtaskfLRQgjxjsIjw/nl5C8ALGy8UN8kx98fXr5U7fbt9YtDiPckiY6wSEeOqLL9AQHw6JFpv7097NsnvTgWY/BguHVL1fUuXVrvaIQQItk69uAY/qH+pHNOR6uSrfQNZo5p+JzZlUYhkhmZoyMszsSJULkyeHmpJMfGBrJmVYsyv3wpSY7ujh+HiAjVdnCAJUskyRFCiPc0//R8AOrlq4e9rc7XoQ8dUveffw4ZMugbixDvQRIdYVF8fGDQINP2lCkq2fH2ht9/l/ntuluwQGWhP/ygKq0JIYR4b4Fhgay8sBKAZsWa6RvMgQOwZYtqjxihbyxCvCcZuiYsyrRppvaZM1CqlG6hiOg0DYYPh7Fj1XZgoKoQYWenb1xCCGEFtl3fRrghnFQOqWhcqLG+wfTvr+6/+QY++EDfWIR4T9KjIyyGpsHy5aq9eLEkORYjqnx0VJIzbJgariZJjhBCJIiv130NQO50ubGz1fGz9d49OHpUtVu00C8OIRKIJDrCIpw4AbVrq89YR0fVFhbAzw/q14dly1Ris2CB1PQWQogE5B/ib2xXy1VNx0hQK3ID5M4NjRrpG4sQCUCGrgldRUTAxx+rIcFRJk+GHDn0i0n8x2CAevVU8YHUqWHdOrUthBAiwQzbM8zYnvPJnDccmQTWrVP3hXRapFSIBCY9OkI3c+aool3Rk5xly6BHD/1iEtHY2qpVWT081B9JkhwhhEhwd/3vAvBFkS+wtdHxa1l4OKxdq9oeHvrFIUQCkh4dkeQiI9XQtL17TftSpYJz5yBfPt3CElFevlR/EFBDF+rUAWdnfWMSQggrdOz+MTZ5bQLg62Jf6xvM+fOq9Cmo4jNCWAHp0RFJKiwMmjUzT3LOnlVFvCTJsQDz5kHBgnD7tmmfJDlCCJHgngU9o+KCisbt0tl0Xo8sanhFhgyQK5e+sQiRQCTREUmqY0dYv9607esLJUvqF4/4j6bBkCHQqRM8fAgLF+odkRBCWDXPmZ7G9qF2hyiQsYB+wRw8CD/+qNpduugXhxAJTIauiSTz77+wdKlp+8EDSJdOt3BElNBQaN8eVqxQ28OHw8iRuoYkhBDW7F+/fwkMCwRgd+vdVPaorF8wmgbV/qv2ljkzDByoXyxCJDBJdESSadlS3dvaqmHA6dPrG49AlY/+7DM1ltDeHn79Fdq10zsqIYSward8bxnbNfPU1DES4O5dUzuqyqYQVkISHZEktm6FQ4dU++efJcmxCA8eQN26cPkypEmjyorWrat3VEIIYfUO3FXzYRoWaKhzJJhKnWbMCJ6euoYiREKTREckiTFjTO1OnfSLQ0STLh24ukL27CoTlclSQgiR6GYdn8WIvSMAqJxTxyFrAPPnw59/qnaDBvrGIkQieKdiBLNnz8bT0xNnZ2cqVKjA8ePH33i8n58f3bp1w93dHScnJwoWLMjWrVvfKWCRPF26pO4//VSNkBIWIFUq+OsvOHpUkhwhhEgCG69upMc21YPSqGAjfqz0o37BvHxpvnDdggX6xSJEIol3orN69Wp69+7NiBEjOH36NCVLlqRevXo8efIk1uPDwsKoU6cOd+7cYd26dXh5eTFv3jxy5Mjx3sGL5OHBA3jxQrWbN9c3lhTvt99g3DjTdtassjCcsBpyEU5Yuubr1UnQyc6JDc024Orgql8wqVNDSIhqHz8Ojo76xSJEIon3tfVp06bRsWNHvvvuOwDmzp3Lli1bWLhwIQNjqdSxcOFCnj9/zuHDh3FwcADAU8aApijRh601aqRfHCmawQBDh8KECWr7o49MVXaEsAJRF+Hmzp1LhQoVmDFjBvXq1cPLy4ssWbLEOD7qIlyWLFlYt24dOXLk4N9//yWdlIIUieTQ3UOERKjE4lC7Q9jZ2ukXzLVrpnaRIlC2rH6xCJGI4tWjExYWxqlTp6hdu7bpBWxtqV27NkeOHIn1OZs3b6ZSpUp069aNrFmzUrx4ccaPH09kZORr3yc0NJSAgACzm0i+fv1V3efMqea8iyQWGgqtWpmSnJEjoWpVXUMSIqFFvwhXtGhR5s6di6urKwtfsyZU1EW4jRs3UqVKFTw9PalevTolZRinSCSzTswytj90/1DHSIApU0zt8+fBxka/WIRIRPFKdHx8fIiMjCRr1qxm+7NmzYq3t3esz7l16xbr1q0jMjKSrVu3MmzYMKZOncrYsWNf+z4TJkzAzc3NePOQoTXJUmSk+dSPXr10CyXl8vWFevVg5Uo1OWrRIhgxQk5qwqokxUU4uQAn3kfHzR1ZdXEVAF3LdsVGz8/gq1dh3jzVHjRIJs4Kq/ZOxQjiw2AwkCVLFn777TfKlClDs2bNGDJkCHPnzn3tcwYNGoS/v7/xdu/evcQOUySCZcvUhaIokugksX//hSpVYN8+1ZW2bRu0bat3VEIkuKS4CCcX4MS70jSNhWdNPYvjao17w9FJYOZMU7tLF/3iECIJxCuNz5QpE3Z2djx+/Nhs/+PHj8mWLVusz3F3d8fBwQE7O9NY1CJFiuDt7U1YWBiOsUx+c3JywsnJKT6hCQs0Z466z5IFzp0DOx2HI6dIBw7AlSuQI4cqH/3BB3pHJITFiH4Rzs7OjjJlyvDgwQMmT57MiBEjYhw/aNAgevfubdwOCAiQZEfEyWavzRg0AwAvB7/UtwDBvn2m8eQtW0oxGmH14tWj4+joSJkyZdi1a5dxn8FgYNeuXVSqVCnW51SpUoUbN25gMBiM+65du4a7u3usSY5I/l6+VOtOnjihirhcuACvyYNFYvr2W/jlF1U+WpIcYcXe9SJcwYIFX3sR7lVOTk6kTZvW7CZEXKy8uBKAjh921DfJAfjyS9A0dQVy9mx9YxEiCcR76Frv3r2ZN28eS5Ys4cqVK3Tp0oWXL18aq7C1bt2aQYMGGY/v0qULz58/p2fPnly7do0tW7Ywfvx4unXrlnA/hbAYx45BxYqwc6farldPfZ6KJLJsGUQv9d65s6oCIYQVk4twwlL5Bvuy5tIaAL794Ft9g9E08PFR7SlTwM1N33iESALxnoHWrFkznj59yvDhw/H29qZUqVJs377dODb67t272Nqa8icPDw927NjBjz/+yAcffECOHDno2bMnAwYMSLifQliEhw+hRg1Vlj9rVlXNuEULvaNKIQwGGDwYJk2CChVg715wdtY7KiGSTO/evWnTpg1ly5alfPnyzJgxI8ZFuBw5cjDhv+qDXbp0YdasWfTs2ZMePXpw/fp1xo8fzw8//KDnjyGsiKZpfLz0YwCc7Z0pn6O8vgEtWmRqN22qWxhCJKV3KrXRvXt3unfvHutje/fujbGvUqVKHD169F3eSiQjq1aZ1h47dUpNDRFJIDRUFRlYpSr68MknIHPcRAojF+GEpdl2Yxtnvc8C8PsXv+Nsr+PFpzFjYPhw1c6YUdZ6ECmGjaZpmt5BvE1AQABubm74+/vLuGgL1rEjzJ+vlmmJZS6vSAzPn8Nnn8H+/apE6Pz50KaN3lEJKyKfv7GT34t4kwhDBA5j1CLpbUu1ZVGTRW95RiK6dw9y5VJtGxu4fBkKF9YvHiESQFw/g6V4unhvT5/C3bvqOzaAg4O+8aQYd+5AgwZqTYS0aWH9eoi2jogQQgh9dPqzk7Hd+oPWOkaCWkcNIE8euHZN1s0RKYr8axfvLCxMzcl5dT2+3Ll1CSfladNGJTk5c6ry0SVK6B2REEKkeMfuH2PRWdWDM6jqIGrmqalvQMuXq/vPPpMkR6Q4ib5gqLBON26oktGvJjk9ekgBgiSzcKHqwTl6VJIcIYSwEG02quHDWVJlYXyt8foG4+enhqoBfP65rqEIoQdJ7UW8aRoUKGDabt0apk6FTJn0iynFuHIFihRR7Xz5THW8hRBC6G7lhZV4PfMCYGT1kfoGA7B0qarKWaAAVKmidzRCJDnp0RHxduiQqd28OSxZIklOojMYYMAA1XOzdave0QghhHjF7xd+p+UfLQFo9UErupTrom9ADx9C376qnT+/vrEIoRNJdES8nD8P1aqZtpcs0S+WFCMkRI0H/OkniIyES5f0jkgIIUQ0gWGB/LjjRwDyps/L/xr8T+eIgGnTIDxctRcu1DcWIXQiQ9dEnEUV+YryzTdSYS3RPX+uFnY7cED9shcsgFat9I5KCCFENIN3Debxy8fYYMOR9kdI55xO34ACA01XInv3VpNqhUiBJNERb3X1qlqLctQo0741a+DLL/WLKUW4fVtlll5eqnz0hg3w8cd6RyWEECKahy8eMu/0PACWfbaMLKmy6BwRcPAg+Pio9siRuoYihJ4k0RFvtGMH1K9vvm/YMPjqK33iSTEePoSKFeHJE/DwUPNyihfXOyohhBCvWHB6ASERIeTPkJ8WJSyk7GjU8IsPP4Q0afSNRQgdSaIjXiskxDzJ+eUXKFsWypTRL6YUw91dDVk7fhy2bIHs2fWOSAghxCu8A70Zvnc4ABVzVsTGxkbniICbN01tuUAmUjhJdMRrzZhhau/YAXXr6hZKyhEerubi2NjA7NkQHCxX44QQwkJNOzLN2J5cZ7KOkUQTvYz04sW6hSGEJZCqayJWt2/DoEGqXaWKJDmJzmCAfv2gSROIiFD77O0lyRFCCAvlF+LHz8d/BmBWg1lkS20BE/7v3IHHj1V79Wp10UyIFEx6dEQMt26ptSij/PWXfrGkCCEhatXVtWvV9t9/Q8OG+sYkhBDijSYfmkxIRAjZ02SnY5mOeoejlCtnastkWiEk0RHmvL3Nk5yBAyFdOt3CsX7PnqlenEOH1JC1hQslyRFCiGRg/pn5ANTwrIGjnaPO0QAnT5oqrfXpI705QiCJjnjFmDGm9ujRqsKaSCS3bqnKONeugZubKh9ds6beUQkhhHiLg3cP8uTlEwA6lO6gczT/iRoVUKMGTJmiayhCWApJdITR0aMwZ45qjxgBQ4fqG49VO3ECPvkEnj5V5aO3bYNixfSOSgghRBx8uca0kFx1z+o6RhLNzp3qPnoxAiFSOClGIACV4FSqZNoeOVJ6vROVvT0EBUHp0irDlCRHCCGShV23dvH4pZrwv+mbTdjaWMBXqfBwU1npjz7SNxYhLIj06AgMBujWzbQ9erR+saQYpUurq2/Fi0tlNSGESCaeBz+n+frmxu1GBRvpGE00zZtDQABkzgy1aukdjRAWwwIuQwg9BQSAnZ1pe+VKmZeTKAwGVa/76FHTvkqVJMkRQohkpN2mdjwNekoGlwz49POxjAVCvb1h/XrVLlfO/KQuRAoniU4KN3u2qf3JJ/DNN/rFYrVCQtQvduJEaNwY/P31jkgIIUQ8PQh4wGavzQAMqTaEjK4ZdY7oP1FzcwCWLdMvDiEskAxdS8Hu3YPBg1U7dWpZLydRvFo+evp0VWFNCCFEsjLuwDg0NFzsXehWrtvbn5BUOv63hs8330CGDPrGIoSFkUQnhYqIgFy5TNvbtukXi9W6eVOtiSPlo4UQIlkLiwxj7sm5ACxpugQneyedI/qPwQChoar9/ff6xiKEBZKhaynUr7+a2lWqQNWq+sVilY4dU3Nwrl1TGeWhQ5LkCCFEMnTH7w5OY53Q0AD4rMhnOkcUzeTJpraUlRYiBkl0UpinT6F/f+je3bRPenMSwf/+p37ZH34o5aOFECIZq7nEdJFqUZNF2NtayGAYTYOfflLt775Tw6OFEGYs5H+rSCpLlphfANqzRwp/JYp58yBnTlXCLnVqvaMRQgjxDrwDvbnjdweApU2X0qpkK30Dim7SJHj+HJycYO5cvaMRwiJJj04Kc+mSui9XDlavhho1dA3HehgMsGqVusIG4OqqTkKS5AghRLK19tJaY/vbD77VMZJYbN2q7j//HBwd9Y1FCAsliU4Ks3ixui9QAL7+WtdQrEdwMDRrphZsGzlS72iEEEIkgIcvHvLD9h8AaF+6vWWsmRMlIgJOnlTt/v31jUUICyaJTgry+LGpXb26fnFYFR8fqF0b1q1TV9QKFdI7IiGEEO/p4N2DFPy5oHH7m+IWtsjchQvqIluaNPDBB3pHI4TFkjk6KYTBoNarjNKhg36xWI2bN6FBA7h+HdKlg40bJYMUQggrUG1RNWN7b5u9VPe0sM/2vn3VfeXKYCvXrIV4HfnfkQIMHQp2djBjhtpu3Fg+F9/b0aNQsaJKcnLnhsOHJckRQggr8OjFI2N7dsPZlpfkBAbCgQOq/eWX+sYihIWTHh0rpmkwZgyMG2fa9/HHMHq0fjFZBV9fqFcPAgJU+egtWyBbNr2jEkIIkQD+uvaXsd21XFcdI3mNc+cgPByyZ5fhGUK8hSQ6Vio8HEqWhCtXTPv274dq1V7/HBFH6dPDzz/D2rXw++9SWU0IIazEs6BnDPhnAABDqg3ROZrXmDlT3ZcurW8cQiQDMoDJSg0ZYp7knDwpSc57MRjg4UPTduvWsHmzJDlCCGElrj27RqbJmfAN8QWgUcFGOkcUi5Yt1UU2UOVThRBvJImOlQkIUAuCRl8U9OlTKFNGv5iSveBg+OorqFLFvHSdJZUaFUII8V6arWtmbH/7wbdUyFlBx2hiERkJK1eatkeN0i8WIZIJSXSszOjR5iX1T5yATJn0iyfZe/oUatWCP/5QPTqnT+sdkRBCiETgE+QDQLtS7Vj22TKdo4nFzp2m9tWrkDatfrEIkUzIHB0r8/SpqX3rFuTJo18syd6NG6p89I0bqnz0pk3w0Ud6RyWEECIRPHyhhicPrDpQ50heY9o0U1vWbBMiTiTRsSKRkbB0qWpPnixJzns5ckTV4fbxUeWjt22DIkX0jkoIIUQiOHT3EAbNAEBqRwude+ntre579NA3DiGSEUl0rMjt26Z2iRL6xZHs7dkDDRtCSIia3PTXX1I+WgghrJSmaTRf39y4nS21BX7eb9kCFy6oduvW+sYiRDIiiY6VCAmBmjVN2/Xq6RdLsleypOrFKVBAykcLIYSVG7p7KPcC7gGw49sd2FhaoZnQUPj0U9O2lJUWIs4k0bESLi6mdoMG+sWRbGmaqYpahgywd6+q4mAv/0WEEMJa+Yf4M/7geAAaF2pM3Xx1dY4oFh9+aGrPmwd2dvrFIkQyI1XXrMCZM6Z2unTm1SdFHAQHwxdfwJw5pn3ZskmSI4QQVu70I1MlzfmN5usYyRvY/vdVzckJ2rfXNxYhkhlJdJK5p0/NL/Y8f66SHRFHT5/Cxx/Dhg3Qr5/5OjlCCCGsWqsNrQComLMimVNl1jmaWBgMcPOmal+4IOu3CRFPkugkY5GRaomXKJ06yWdgvFy/DpUqwdGjkD49bN8OWbPqHZUQQogkMO/UPB68eABAw/wNdY7mNZ49U6MOADw89I1FiGRIxuYkY5s2mYqw1KgBv/6qazjJy5Ej0KiROonkyQNbt0LhwnpHJYQQIolMODgBgE8KfMLgaoN1juY1/vjD1HZ21i8OIZIp6dFJphYtUtNKANzdYfdufeNJVtavV8PVnj2DsmVV0iNJjhBCpBgnH57ktp9ak2FUjVHY2VroBP/OndW9jDYQ4p1IopMMrVwJ7dqZtn/5RYasxcutW6oed6NGqrqanECEsAqzZ8/G09MTZ2dnKlSowPHjx+P0vFWrVmFjY0PTpk0TN0BhMT5daSrXXDJbSR0jeYOICFN76lT94hAiGZNEJ5kJCYGWLU3bf/4JTZroF0+y1LcvrF6tChCkSqV3NEKIBLB69Wp69+7NiBEjOH36NCVLlqRevXo8efLkjc+7c+cOffv2pVq1akkUqdDbkXtHePxSFZ7Z9M0m7G0tdBT/y5emdtQQDiFEvLxToiNXzfQzfrypPWWK+Rpi4jWCglRFtYAAtW1jA19/LWsRCGFFpk2bRseOHfnuu+8oWrQoc+fOxdXVlYULF772OZGRkbRs2ZJRo0aRN2/eJIxW6Ony08sAuNi70LhQY52jeYP9+9V9qlSqtLQQIt7inejIVTN9jRmj7j/7DHr31jeWZOHJEzUfZ8oUaNVK72iEEIkgLCyMU6dOUbt2beM+W1tbateuzZEjR177vNGjR5MlSxbax2FtktDQUAICAsxuInmKSnTalW73liN1FnXC/+ILGZ8uxDuKd6IjV830E70Xe8gQ+dx7q2vXVPnoY8cgQwbVqyOEsDo+Pj5ERkaS9ZX5dlmzZsXb2zvW5xw8eJAFCxYwb968OL3HhAkTcHNzM948pNRvsuQX4seSc0sAqOJRRedo3sDfH06cUO1Jk/SNRYhkLF6JTlJcNQO5cvY6O3eqexsbKGmhcyctxqFDKsm5dUuVjz58GKpW1TsqIYQFePHiBa1atWLevHlkypQpTs8ZNGgQ/v7+xtu9e/cSOUqRGJafX86z4GdkT5OdJoUteILryZPqPk8eyJZN31iESMbiNQPvTVfNrl69Gutzoq6anT17Ns7vM2HCBEaNGhWf0KxeZKQargaQKxfYW+jcSYuwbh18+y2EhkK5cqpig1RWE8JqZcqUCTs7Ox4/fmy2//Hjx2SL5UvizZs3uXPnDo0aNTLuMxgMANjb2+Pl5UW+fPnMnuPk5ISTzJNI9lZeWAlAi+ItcHVw1TmaN9i+Xd2XK6dvHEIkc4lade1drpqBXDl7VXg45Mhh2q5ZU79YLF5QEPz4o0pyGjWCPXskyRHCyjk6OlKmTBl27dpl3GcwGNi1axeVKlWKcXzhwoW5cOECZ8+eNd4aN25MzZo1OXv2rAxLs1LzT8/nyH01+qRE1hI6R/MGDx7AjBmqLRWHhHgv8eoXSIqrZiBXzqLcuwdLlsCwYaZ9FSrAggX6xWTxXF3hr79g+XKYOFEqqwmRQvTu3Zs2bdpQtmxZypcvz4wZM3j58iXfffcdAK1btyZHjhxMmDABZ2dnihcvbvb8dOnSAcTYL6zHhIMTjO1vP/hWx0je4rvv1Bo6+fLBV1/pHY0QyVq8Ep3oV82iSkRHXTXr3r17jOOjrppFN3ToUF68eMHMmTPlqtkb+PmpIWqv2r0bbGX1I3NBQXDqFERV9CtZUiYxCZHCNGvWjKdPnzJ8+HC8vb0pVaoU27dvNw61vnv3Lrby4ZliBYUHccv3FgBbW2zF1saC/y3cvavuq1UDZ2d9YxEimYv3TA+5apb4vL2hbFnTdoMGMHYslC4tldZiePJEDVE7d05lgZUr6x2REEIn3bt3j/WiG8DevXvf+NzFixcnfEDCYozZp0o1uzq4Uj9/fZ2jeYMbN8DLS7Vf829ZCBF38U505KpZ4vLygsKFTduFC8PWrfrFY9G8vKBhQ1VZLUMG0DS9IxJCCGGBdt1W87eq566OjSVfMaxe3dQuVUq3MISwFjaaZvnfDgMCAnBzc8Pf35+0adPqHU6iGTZM9dxE+fFH6NkTcufWLyaLdfAgNGkCz59D3rywbRsULKh3VEJYnZTy+Rtf8ntJPi49uUTxX9QokjPfn6FUtlL6BvQ6mmYam96wIWzZom88QliwuH4GS5FiCxAZCZs2mSc5rVrBtGn6xWTR1q5Vv6DQUFWdYfNmyJJF76iEEEJYoG83qMIDpbKVstwkB9RK4FF+/lm/OISwIpLo6OyXX6BHD5XsRPH2lorIr7V3L3z9tWo3aQIrV6pKa0IIIcQrtl7fylnvswAsbrJY11jeaoKpKhx58+oXhxBWRBIdnfXrZ0pyGjeGli0lyXmjjz6CL76A7Nlh+nQpHy2EECJWvsG+fLLyEwDalGxDyWwWXI3zr79M7Q0b9ItDCCsjiY5ODAYoUwZevlTbBw5A1ar6xmSxXr4Ee3twclLjl1etUgmOJU8oFUIIoase23qY2uV7vOFIC+DtbWr/t3yHEOL9SXk0nYwcCWfPmrbLl9crEgv3+DHUrAnt2pmqqtnbS5IjhBDitcIiw1hxYQUALUq0oEz2MjpH9Bb796v7/5bqEEIkDOnR0YGmwZgxpu0HD8DRUb94LJaXl1pE6PZtVUL6zh3Ik0fvqIQQQli4ln+0BCCDSwYWNl6oczRxsGyZuq9YUd84hLAy0qOjg1OnTO0hQ9R0E/GKAwegUiWV5OTLB0eOSJIjhBDirXyCfFh3eR0Acz+Zi5O9k84RvcWDB6Z2uXL6xSGEFZJERwf+/qb28OH6xWGxVq+G2rXB11eVjz5yBAoU0DsqIYQQyUC3rd0AyJ8hP18V+0rnaN7i9GnImdO0LYuECpGgJNHRwZMn6r5CBRmyFsOsWfDNNxAWBp99Brt3Q+bMekclhBAiGbjte5s1l9YAUCxzMZ2jeYvAQFWVKMpXX8n8UyESmCQ6SUzToEUL1ZYLN7EoUQIcHOCHH9TCoLJGjhBCiDg6/uA4AC72Lqz7ep3O0bxFw4am9p9/wpo1+sUihJWSYgRJLHoFSUl0YlG9Opw/D4UL6x2JEEKIZGbe6XkAdC3XFXtbC/6Kc+6cmosK0K0bfPqpvvEIYaWkRyeJzZ9vanfsqF8cFsPbG+rWhUuXTPskyRFCCBFPm702s+v2LmxtbC173ZyxY82vdP7vf7qFIoS1k0QnCd29ayo+YGur1rxM0a5eVZXVdu6ENm1M6+QIIYQQ8bDx6kaarGoCQN70ecmdLrfOEb2Gry8MG2baHjNGfSEQQiQKC+7XtS7nz0PJkqbtDRv0i8Ui7N+vVn/29YX8+WHVKpmEKYQQIt62Xt/KZ6s/M27vbLVTx2jeICgIMmQwbXt5QcGC+sUjRAoglxGSSPQkp3lzaNxYv1h0t2oV1KmjkpyKFeHwYZXsCCGEEPHUZmMbANI4puFh74d4pvPUN6DXGT/e1B4yRJIcIZKAJDpJwMvL1P7xR1i5Ur9YdKVp8NNPKtMLC4PPP5fy0UIIId7Zg4AH+AT5AHCswzHc07jrHNFrtGoF48ap9scfq3k60URGwvPnOsQlhJWTRCcJRJ9bP3WqfnHoLjIStm1T7V69VClNFxddQxJCCJE8+QT5kHO6WmwzlUMq8mXIp3NErzFwICxfbtp+JcnZvx/s7SFjRli4EH75JYnjE8KKyRydRBa9mFiKXwvM3l5NTtq4Edq21TsaIYQQyZSmaZSfV964Pe7jcTjaWeAK3CEhaiHsKN7ekDUroNYL/f136NTJ9HD79uq+a1epzyNEQpAenUT06BEUL27aXr1av1h04+1tXjozXTpJcoQQQryXy08vc9vvNgBNCjWhe/nuOkcUC01TQ7RfvlTbQUHGJOflS0iTxjzJedXEiUkQoxBWThKdRNSihak9YUIK7M25fFkVG+jZE+bO1TsaIYQQViIgNABQQ9Y2frMRO1sLXK9h7VrTcO0PPwQXFwICoGZNSJ367U8fNEhNZxVCvDtJdBLR3r3qPn9+NUQ3Rdm3D6pUgX//hQIFoHZtvSMSQghhJVZdXAWoNXMskqbBDz+Yttev5+hRcHMzfTeIYmsLO3aoWgUnT5qPdGvXLkmiFcJqSaKTCHx9zXtvtmzRLxZdrFwJdeuCn59aEFTKRwshhEgg6y+v53/H1ZDo+vnr6xyNEhEBR4+CwfDfjvnz4fFj1b50iTodPalUKebz9u1TdXrq1oXBg6FMGejWDbJkUY+vWJEk4QthtSTRSQSdO5vaJUumoFL5mqYGFbdsqfrbv/gCdu2CTJn0jkwIIYQVOPPoDF+u/RKAarmqMbG2ZUxkKVtWXdezs4NTyy7ze6fdfM56BjjNwKZYUf75x/z4KVPg9m346KPYX2/TJlPbxkYdK4SIPxtNs/y6HgEBAbi5ueHv70/atGn1DuetonpzbG3VlZoU4/Rp9WmvadC7N0yerH4JQohkK7l9/iYV+b0kvdCIUJzHORu3n/Z7SiZX/S+kTZigemPiYv16aNAgbisrvDqvd/t2Nb/H0QKLywmR1OL6GSzlpRPYlSum9o4d+sWhiw8/hGnTVHITfWyyEEII8Z7ab25vbC//bLluSY7BYLqG17w5rFoVt+d17KiKsMVVnz7ma+/VjzZKz8YGAgLiVtRAiJRMLrcnsKhKyo6OUKOGrqEkjUeP4N4903avXpLkCCGESFCRhkjWX1kPQP/K/Wn5QUtd4vj4YzU8zcZG3aInOY0KXHn9E4Gff47fe02Z8vrHNE2Vp37+PH6vKURKI4lOAouqotywoVof06pFlY9u2BD8/fWORgghhJVafWk1IREhONg60L9Kf11iyJUL9uyJ/bEhQ2CzR3fOU4KQTj+gaSoZMRgwtp2c4v+exuIGr5ExoywsKsSbSKKTgKJ3bLRv//rjrMLevVC5Mty9C6GhqtScEEIIkQj+uaVm8zcv0ZyMrhkT/f1CQ9V617/+auq9iX6Oj27SJBjb7hbs3k0JLuJUu5rxsfddP8/GBpo0Ue3Xfa+wtVUrOQghYpJEJwH9+KOp/ckn+sWR6KLKR/v7q2Tn8GHw9NQ7KiGEEFZI0zS23VALb7YskThD1iIi1CntxQu17ewM7u7mVVSjjB4NV6+atnt9fhfy5TPt+PTTBI1t40bVazN/vql36OhR82M8Pd/e+yNESiSJTgLx9YXNm1W7TJn3v4pjkTRNlZdp2RLCw+HLL+Gff6R8tBBCiETzx5U/8A70xt7Wnqq5qib46xsM4OAA6dJB2rSvz1Ny51ZzYoYNg0KF/ks6XgbhWCC36aCVK+NWUu09VagAq1eb77Ozg9mzE/2thUhWJNFJIFu2qO/+AEeO6BtLopk0yVRDs29f9SmbBB/oQgghUiaDZmDy4ckARBgicHVwTdDXDwlRCUJ0sS3yvWwZ3LkD6dNH2/niBaRKZdru0EGVYUsiX38dc35O9+5J9vZCJAuS6CSQVq1MbQcH/eJIVK1bq/7xn3+WNXKEEEIkuhF7RnDswTEABlYZmOCv37fv6x9r0ED19jx6BN9+G8sBn31man/9Ncybl+DxxcWZM+bb9etDt27g4yPD2YSw9rpgSeL8eVM7ep17qxAcbOq1yZ4dLl0C14S9oiaEEELEZuoRtZCMDTYMrJpwiU5ICJQvDxcumPbNmqWGpu3Yoa7p/fKLGoaeLVssL3DxIuzapdqffw7LlydYbPFVqhT4+UHx4nD/vmkNvzlzoEcP07IXQqREckn+Pfn6QsmSpu01a/SLJcFdugRFipgPBJYkRwghRBIIiwwjOCIYgN1tduPm7JZgr/3TT+ZJzpw5qhdk2DA4eFDlLWnSvOEFxowxtdev130oh5tb7FXZfv4ZIiOTPh4hLIUkOu+pWzdTu1+/t3wwJid79kCVKqpm5fjxqiSNEEIIkUQuPblkbH+U+6MEe93Tp2HECNP2b7/FXl3tjaJ6c6ZOTbC43tfryk/312fZISEsgiQ67+n339W9s7NaMMwqLF8O9eqpWptVq8Lu3Slg9VMhhBCW5Mcdas2G2nlrY2uTMF9XHj9WlVGjzJoFHTvGs1JqvXrw7Jlqt2uXIHElBA8PdU0yIMC8SMG0aWqYnhApkSQ67yF6d/CyZarrOFnTNBg3TlVWCA9Xkyt37lRLLwshhBBJ5E+vP9n37z4A6uatmyCveft2zPk20UdlxMnhw/D336r96aeqJrUFsbMzjSy5fNm0/8QJ9R3l1Cl94hJCL5LovIfoHxgNG+oXR4IwGKBTJxg6VG337au6q5yd9Y1LCCFEivI8+DntN5vGYfWt/IbSaPGQN6/59tOn8XyBc+fUkO4omza9d0yJqUgR2LbNtB0QAGXLqt4rqcYmUgpJdN7DgQPqvnBhK5ijb2sLGTKo+1mzpHy0EEKIJLf79m4y/pSRp0FPyZIqC34D/LBJgBW4AwLMt1+8iOda1xERqrxZlBMnksU5sn590xD76GS9HZFSWP7/Ugs2YIC6z5xZ3zgSzIQJarXTePflCyGEEO9n6O6h1Fpay7i9+ZvNCVJp7cUL86Hl//4LqVPH4wU6dTKvqrZggeoaSSa++cY0WCPKL7+oegrXr6shbvv3qwqyO3fqE6MQiUUSnXd08KBpjk6nTvrG8s4uXoQWLdSCAqCuTsmMRSGEEElM0zTGHRhn3D7e4TgVclZIkNcuXNh8O1eueAVmvhBo374WVYAgrsaMUT+Kl5dpX+3aULAgFCsG1aurNQHr1oXhw/WLU4iEJonOO3j6FKpVM21HXxw52di9W401/v13tXCAEEIIoZNfT/1qbL8c/JJyOcq992tqmioi+vChaV9wcDxfZO5cU/v+fTWsOxkrWDBm786rxoyBa9eSJh4hEpskOu8g+tWhSZMgVSr9Ynkny5apgbsBAap89KBBekckhBAihbrle4uuW7oC8GPFH3F1SJhJr+XKqSKiAOnTqwn48aqvExEBvXqpdt26kCNHgsSlt9Gj335M69aJH4cQSUESnXj6+294/ly1GzdOZgtxaRqMHas+waKXj86QQe/IhBDivc2ePRtPT0+cnZ2pUKECx48ff+2x8+bNo1q1aqRPn5706dNTu3btNx4vEs/KCyvRUAu/jKk5JkFe08vLvDLqkiXxXCvnwgU1LycsTNVsjm1GfzIVVXUte3b44QfV42UwmFdiCw3VLz4hEpIkOvFgMKh1wqKsX69fLPEWHq5WRYsaptavn5SPFkJYjdWrV9O7d29GjBjB6dOnKVmyJPXq1ePJkyexHr93716aN2/Onj17OHLkCB4eHtStW5cHDx4kceQp223f28w4OgOAkdVHksrx/YdI/POP+ciLrl2hQYN4vEBAgPmaEc2bW90FQRsbePAAZs4Ed3e1bWOj6hEBnD1rvg6PEMmVjaZFXz/XMgUEBODm5oa/vz9p06bVLY4tW9T6YKAm640apVso8Xf7tloO2t8ffv5ZffILIcRbWMrn79tUqFCBcuXKMWvWLAAMBgMeHh706NGDgQMHvvX5kZGRpE+fnlmzZtE6DuN2ksvvxVL5Bvsycu9IZp+YTaSmKvvc+uEWedLnea/Xff7cfI3r3buhZs14vki+fHDrlmr/8w/UqvXm461IQIB5hTqDIZ49YUIkkbh+BtsnYUzJXvSe62Q3fz9PHrW4ma+vGnMnhBBWIiwsjFOnTjEo2nxDW1tbateuzZGoS9RvERQURHh4OBlec+U+NDSU0GjjeQJeXZhFxFl4ZDgV5lfg+vPrALindmdLiy3vneQ8eqTykihffx3PJEfToGlTU5LTrl2KSnIA0qZVw9n+9z+1bWurksf06fWNS4h3JUPX4mHFCnVfowbYJ4cU8eJF2LvXtF2tmiQ5Qgir4+PjQ2RkJFmzZjXbnzVrVry9veP0GgMGDCB79uzUrl071scnTJiAm5ub8ebh4fHecadEmqbRZUsXY5Kz5ss1/NvrX0q7l36H11JTaZYsAScnNeckqjMud25YtSqeL7h7N2zerNply5pXXEtBZs4037ayUXsihXmnRCclTvjct8/UbtJEvzjibNcuVT66SROV8AghhIjVxIkTWbVqFRs2bMD5NfMWBw0ahL+/v/F27969JI4y+TNoBqYfnc6CMwsAqJqrKl8V+woHO4e3PDN2bdrABx9A27aqZkB0Eye+w5CriRPVfePGcOKE+SKhKcyWLebbFSvqE4cQ7yveiU5Km/BpMKiLOjVqmPb17KlbOHGzdKmpfHSpUuoylxBCWKlMmTJhZ2fH48ePzfY/fvyYbNmyvfG5U6ZMYeLEifz999988MEHrz3OycmJtGnTmt1E/PTf2Z8+f/cBoHbe2mxvuf2dX+vePbVSQmzKlIFmzd7hRS9dUvcdOrxzXNaiYUPzHrFjx1TiKNdNRXIT70Rn2rRpdOzYke+++46iRYsyd+5cXF1dWbhwYazHr1ixgq5du1KqVCkKFy7M/PnzMRgM7Nq1672DT2yaBpUrQ5cupn3LllnwxDxNUyt9tWmj6v9/842qhy39zkIIK+bo6EiZMmXMzitR55lKlSq99nk//fQTY8aMYfv27ZQtWzYpQk2xDt49yNQjUwHInyE/f3z9x3tVWLt/P+a+kBCVAB058g7n6YgItRo4QOn4D6OzRs2awciR5vtKlIC1a3UJR4h3Eq9EJ2rCZ/QxzAk94RPUpM+AgACzmx5u31ZXMaIcPQrffqtLKG8XHq6uQg0frrYHDFCTipyc9I1LCCGSQO/evZk3bx5LlizhypUrdOnShZcvX/Ldd98B0Lp1a7NiBZMmTWLYsGEsXLgQT09PvL298fb2JjAwUK8fwWpt9tpMtUXVjNu7Wu8ijVOad3qtly9VTZ3KldV2qVJq8IKvrzrd5cz5jiPOli9XyY6bm4yCiGbECFP+F+Xrr83X3BHCksUr0UmKCZ9gOZM+lyxR905OqrOkQgVdwoibWbNg4UJVIuWXX9RYY1upNSGESBmaNWvGlClTGD58OKVKleLs2bNs377deL66e/cujx49Mh7/yy+/EBYWxpdffom7u7vxNmXKFL1+BKs1+fBkY/vP5n+Syy3XO73OnDmQOrX5IIWmTSFNGkiX7j0C3LMH/kuIqVpVzp2vyJQJgoOhe3fTvl9+0S8eIeIjXuvoPHz4kBw5cnD48GGz4QD9+/dn3759HIve/RGLiRMn8tNPP7F37943joWOrYynh4dHkq5XoGmmz7rSpeH06SR523cXFqYus3ToYFrsRwgh3pOsFxM7+b3EjXegN+5T3QHY0GwDTQs3fafXOXAAPvrIfF+xYqrq2nsNJ//zT/NqpI8ewVvmdaVUkZHmFWfv3VM9aELoIa6fwfG6bJEUEz7BMiZ9Rh+iZrEVmW/eVJ88AI6OsHGjJDlCCCEswp7beyg6uygAGVwy0LjQu59M69Qx3x45MgGSnKFDzU/wR49KkvMGdnawbZtpu3p1/WIRIq7ileiklAmfERGwcqVpOw6Laie9f/6BDz+EH39U3U9CCCGEhThy7wgfL/0Y3xBf8mfIz+ovV2NrE/8hYaGh8Ntv6h5UmWNNU3NH3ivJuXQJxo0zbZ8/b+Hj0y1D/fqmKUzPnukbixBxEe9lL3v37k2bNm0oW7Ys5cuXZ8aMGTEmfObIkYMJEyYAasLn8OHDWblypXHCJ0Dq1KlJnTp1Av4oCcfLy9Q+dw5es6yCfhYvho4dVUZ27pw6A1hckEIIIVKq5uubG9tnvj9Dasd3O98XLgx37pi216x5z8AAbtyA4sVN27dvg6dnArxwynDkiFqQ1d9fJZvnzqn1jISwRPG+vJISJnxevaru06SxsP+8mgajRqlJkxER0Ly5Kh8tSY4QQggLsfriav71/xeA9V+vj1eSExqqTm+BgWrRyuhJDkCC1CYqUMDUXrhQkpx4ypVLDWOLUrIkbNigXzxCvEm8ihHoJaknfZYqpa5QNGgAW7cm+tvFTXg4dOqkenNAjacbN06qwwghEpVMuo+d/F5id/T+UT5a9BHhhnAaFmjIlhZb4vxcV1dV3Ss2RYrAyZPqmPeyZw98/LFqd+miSrmJePvnn5jzpgwGC15nUFidRClGkFJcv67uLerc9dVXKsmxtYW5c2HCBElyhBBCWJRxB8YRbggnR5ocTKs7LU7PMRhU0dDXJTkNGsDlywmQ5Jw7Z0pyAGbPfs8XTLlq1zYtqh4l+pQnISyFfFN+xfXrEBSk2gMG6BuLme++U2PpNm+G77/XOxohhBDCjH+IP39d+wuA2Q1nUyhToTcef/Ag7N6thkGtXRv7MZs2JdDIil271HCNKLt3S/dDAjh40NQeNkyGsAnLE+9iBNauYEFTO/owXl1ERJiK1jdpoiZMZsyob0xCCCHEK4LDg8n/c37jdpnsZV57bEQENGwIO3fGfOzQIfNeggTx/Lnqgoiyfj3UrJnAb5Iy2djA/fum9XQ+/1zW1xGWRXp0ookqQgDQo4dagVk3O3eq1dD+/de0T5IcIYQQFmjmsZn4BPkAsKTpEnKmjf2b7osXkC5d7EnOokUJnOQEBkKzZubnzh071LdxkWBy5IC//jJte3hA+vTQv79+MQkRRRId1DjTyZPVZMco48frFw+LFqnLXdeuyaBXIYQQFsugGWi4oiGDdg0CYGzNsbQu2drsmMhIePBADQ1v0ABevlT73dxg7151PS80FNq2TcDAFi5Uw72j16MeOBDq1k3ANxFRPvnEfNvPT32vsrGBixd1CUkIQBIdAM6cMb/yMGWKTr05mqaWe27XTvXtt2gBP/+sQyBCCCHE2/168le23dgGQJ28dehVsVeMY3r0UEOZChZUQ9MAKlVS5aOrV1flih0dEzCo06ehfXvT9jffqEoH/63vJxJHSAh07gyFXpmaVaKEum4rhB5SfKJz+TKUiTaU+MYN6NNHh0DCwlSCM2qU2h4yBJYvBycnHYIRQggh3m7FhRUA1MpTi79b/U0qx1TGxx48gN9/h19+MX/OsGFw+DBUqZLAwQQGQv785if1xYvh119lvbkk4OSk/tZXr8LNm+ZV8goVguHDVc/d4MHQoYNKjIRIbCm6GMHYseoDN8oPP0C+fDoEEhAAX3yhCtPb2am6/p066RCIEEIIETezj8/m0D3VRdO3cl+zxyZNUiPFXuXhYX7eTVAtWqhv2FH27IEaNRLpzcSb5M2rhiju2mWqAzFmjLpFcXSUZYxE4kuxPTo+PuYftgMGwMyZOgVja6uqwqRKBX/+KUmOEEIIi+UT5EPj3xvTfVt3AHK75aaMu6kXxd8/ZpIzYgQ8fgx374KDQwIH5OenRkT8+afaLlxYfcuWJEd3tWqpwSmx+eUXVSvizh01cl+IxGCjaZb/zysxVqDOlk196IJKenQvaPboEXh7Q+nSOgcihBAmifH5aw1S6u9l9vHZDNo1iBdhLwCokKMCB747gIOdyl6ePIGsWU3HT5gQe89OgkqfXiU7UW7fBk/PRH5TER/h4WoYY5s2qtjT4MExjwkOlhGGIu7i+hmcInt0AgNNSc4nn+iU5Pz9N0yLtmq0u7skOUIIISxWty3d6L6tOy/CXuDm5EaXsl3Y2WqnMckBaB2t4FqfPkmQ5Dx7ZkpyqldXhQgkybE4Dg7q34amwaBBsVdi69kz6eMS1i9FztGJfuFn9WodAli4UA1Pi4xUa+XUq6dDEEIIIUTcbLiygTkn1YQKRztHfPr7YG9r/hXi2DG1TA1AxYowcWIiB2UwmDIpd3c1J8fGJpHfVCSEYsVUL8+hQ6YRhr/9BqNHm/cICvG+UmSPztmz6j5nTvOqIIlO01TZkfbtVZLz7beyOrMQQgiLFmmI5PM1apFNz3SeBA0OMktybtxQp7WKFU3PmTMH7BPrUuqjR2pyh50dzJ+v9s2dK0lOMmNvrzrh/P1NyU22bLLQqEhYKbJHZ+9edZ8zZxJ+LoaFqXqKy5ap7SFDVPkR+WAWQghhoUIjQqm33DTqYNUXq7CztTNunzwJ5cqZP6dGjUQcib1/v/p2HF2jRtC4cSK9oUhsadPCtm3w4Ydqe/JklcPKskciIaS4Hp3AQJg6VbXd3ZPoTf391XLQy5ap/72//aZqW0uSI4QQwoLNPz2fff/uA2BynclUyFnB7PFXc44dO9QIskQRHm7+ho0bq8kemzYl0huKpFK6tPmiohMnqq9INjawcqUaDDN6tFRnE/GX4hKd//3P1B40KInedPt22L0bUqdW5S87dkyiNxZCCCHiL8IQwS8nfmHUPrWIdbNizczWynnxAtzcIChIbZctqxYIrVs3kQIKD1cLr0S5elUlOMWKyUVDK1GggEpmXtWypRoAM2KEWo3jt9+SPjaRfKW4RCdqOG+FCuqDOUk0awY//aS63Bs0SKI3FUIIId5Nyz9a0nVrV54GPcXZ3plhlcdStarKKfLnV8ONAgLUsfnywdatkD17IgVjMJgnOT/+CIUKJdKbCT0NG6amMHfr9vpjvv9eFdsTIi5SVKLz4oUqrw/w2WeJfBFo9261QE+Ufv2kfLQQQgiLt+XaFtZcWgNAyxItOfP9GeaMy8+hQ+rxmzfNj/fygsyZEzGgpk1NbQ8P86UZhNWxtYVZs1R+O20adO4M+/aZ125q3lw9LsTbpKgFQ729TfNynj6FTJkSKMBXLVigLjmULw+7doGLSyK9kRBCJK6UujDm21jr7+VBwAOKzilKQKjqrgnqb8DT04YnT8yP27RJzZdo0iSRAzp2zFTO7cMP4dSpRH5DYcnOn4eSJVV72jTVuSdSprh+BqeoqmuLF5vaiZLkRJWPHjtWbRcooIoPCCGEEBYuIDSAnNNzGrdv9LjBzJnmSc7mzVC1KqRPn0RB1a9vav/9dxK9qbBUH3xgGjbZu7cqMDV0qEzTEq+XYoauBQcncvGBsDBo08aU5AwbpjKr6OOKhRBCCAv1y4lfjO2NX25h28p8TJlievzcOVXJOcmSnJ49TSt837oFGTMm0RsLS7Zli6k9fDg4OxOjx1GIKCki0TEYzBcGXbo0gd/Az09ddYoqH71ggSodIpcYhBBCJAOj941m4K6BAAwoN5qmxRvSo4ea9O3iAtevq6vpSWb/flOZ1Ny5IU+eJHxzYcmqVoWdO03bYWFqWkLfvqqHR4joUkSis26dqd2zJ7RqlcBv0LatWjggdWp1qaFduwR+AyGEECLh+Yf403NbT0bsHQGA24mJTPpkmPHx1q3h7FlVaS3JvLoo6MWLSfjmIjmoXVtdxI5aJ9ZgUGskurnJWjvCnNUnOsHBqrpzlBkzEuFNJk9Wl7oOHIB69d5+vBBCCGEBPv39U/53/L+eE+8S+G8ZYPb44sVQsGASBnTzpnmSs2yZuogoxCtsbFRRjOBgU+efwaCqtuXPrwpQCWH1iU70IWtTpybgCz96ZGoXKABnzkCpUgn4BkIIIUTi2XtnLwfvHgSg4KUlMPe88bEBA9SV8SQbgR0RoSbSRu86+vVX+PbbJApAJFfOztC9u/m+mzfVcLZ8+eD331WFtocP9YlP6MuqE539+03twoVVhY4EMW8e5M0LO3aY9tla9a9SCCGElQgOD+bH7T9Sc0lNCHYj1c5FXFvb2vj4zp0wcWISBuTjAw4O5m/6xx/QqVMSBiGSMxsbCA+P2fl36xa0aAF9+kCOHGoatY0N9O8PDx7oE6tIWlb97Tz6MLWTJxPgBTVN1THs1AlCQlSdTSGEECKZeBn2ktrLajPj2AwAXNbu4uWhtsbHR41S8x+SjKap7qPofHzUqt5CxIO9vVoYXtNUdUCAbNmgUCHTMVGLjE6erOpbjBuX9HGKpGXViY6Dg7rv0wdSpXrPFwsLU7Myo/5XDB+ulu4VQgghkoGVF1ZScFZBDt87TGrH1PTJ9hfBt8oA6othcLA6tSUJg0ENTXN2hoUL1b4ePdS3VCkjLd7T5s3qn9KjR3D1qhoZefgwlCkDX38NVaqoHqChQ2HIEFMCJKyPVSc6hw+re0/P93yhqPLRy5erSwYLFqjLXlI+WgghhIULCA2g/ab2tPyjJQ9fPIRwF6pfOcPUzp8AakjPsmUq50hUjx6p6gatWqkxRp07q4uIABUqwODBiRyASKns7KBSJTW6Z/VqOHjQNK9n/Hj1eNOmcPSolKi2NvZ6B5BYNA3u31ft8PD3eCE/P5X6X74MadKoWtV16yZEiEIIIUSim3RwEgvP/tdrEpqKEn97s+WUmsxQuDDs3q3K8iaqpUvVotqvcnGBEyegWLFEDkAIcxMnqn/7ly+r7U2b1C3K1KlqZFD37nJdOzmz2kRn0CBT+73yEjc3dRnAzw+2boWSJd83NCGEECLRzTw6k8P3D7Pm0how2OCwbgvhlxtwAdWhMnOmWn7hvYd2v82hQ+ZJTuPGkCGDGifn6fn/9u49LKo6/wP4m9vMqD9Ak7iKmvcbhooiWGpFsVmu/qoV00Vqd8v9Sf1UdivvVJSo0S5PhpV3t5+GWuaz65KaKOvjbX0EKQPFxXvqkKYLCArCfH9/fJcZBgZkxjlnmOH9ep7zMHPOd2Y+83E83/nO+V74LZIcokMH4Pvv5e/Xkyc3Pv6HP8i/K1bIr38hIYBGo26MdP/chGj9SyuVlZXB19cXpaWl8PHxuWf5U6eA/v3l7QcfBH76yYYXrT+v5t27cnBkUJANT0RE5LysPf+2Fa09L/Oy5yH1QKq8ow8zmzrazQ346isVxvvn5QF79phPNvDjj/IbI1ErdP68nKTAEjc34IkngA0bgOBgVcMiC1p6Dna5MTrl5aZGDgAUFNjwJCtXAs89J0evAfLaJRs5RETkBG7evonUr7cBHxcCR//HrJEDyJ5iijVyqqqALVuA6dPlyO/6jZzvv2cjh1q17t3l79wGg+zIU0erlfv37AEGDAD++EegosJRUZI1XK6h8+STptuLFskrOi1mMMjBkNOnA9u3y1WmiIiInMi4TeOAjFPA9f5A1grj/qVL5Ze1YcMUeNHKSiAlRc5oEBcnfzCsM2mSnB0oLEyBFyayPzc3OXJBCLndvi17YA4YAJSWyvE7XbsCjzwiu7hduODoiKkpLtd1ra63maenlZMQVFUBv/kNsGmTvP/227KlxL7DRNSGtfYuWo7SWvPynf47hD+/Czj0pnHf5MnACy8Azz+vwAteuyafvP4K3XXi4uRK3SNGKPDCROqrqpITFiQmyhEN9R05AgwdCpw4IRtGo0Y5Jsa2oqXnYJebjECrlR/EuqmlW+TmTXkd/x//kC2kVauAl15SKkQiIiK7K71TivD5M4BDB4377tyR9aLdGAzAjRvApUvySk1xsfnxvn2B1avlT91ELkarlR/7xx8H/vY3+YP69Ony2MiRchLB27fl/cWLZRe3ujUda2qA7Gw502G3bo6Jvy1yuYZOVZX8GxDQwgdcuAA8/TRw8qScPvqrr8z7vxERETmBeZ/tB9aaGjmXL9u5kZOX13S/t0GDgNxcTktFbYKfH/Dyy/J2ZSUwe7a8XdfIAeRIiHnz5ASDQUHmY8Y//1xO6Nu9u1zDh5TjUg2dhpcRW+TaNdnYCQmR8wcOHmz3uIiIiJS0Zsd3WDFrvPG+t7cdZ4aqrQW++QYYP77xsaeeAt59Vy74SdQGzZolu4UeOybH8ISEyKs+33wjj9+4Ibf64uNNt3v2BCIigE6d5FfSXr1kF7ghQ+Qxd5cbTa8ul2ro7Ntnut3iiV0iIuT1xz59gC5dFImLiIhICXtzL2DW2+dxYscY474Bg2pQcOI+qvfz52V/nDt3ZCPn4EHz43/+MzBzprzNcaxECA2VW52sLKC6Wk5AWFoqu6s9+KD8QT49HSgqAk6flmXPnJGbJYMHy6+oXbsq/hZclstMRmAwmC7/ubvLc3OTVq0CwsOB4cPtHisRkStprYPuHU3tvFTfrcUP567hu9M/I/vwz8jZ64XLR6IalevY+S5uXvey/gWEAHbvBl5/HfjXvyyX+cUvgClTgF//mg0covt0+rQcx+PmBpw9K/9OnCgbQXl5wNGjprJ/+Yu8utOjh1zst317Xulpc5MR5Oaabr/4YhOFDAZg/nxgyRLA31/O6d/iwTxERNSaZWRk4IMPPoBer8fDDz+M5cuXY0QzM35t3boVCxcuxPnz59G7d28sXboU48aNUzHipuWd1mPq/57Gj8WdcPvnB1BbFgAYAgEEWiw/9L9zsCk9DH27djbtrKyUi8uVlQG7dgEXL8pFdCz9EvjPf8qfoOt7+mnTQIThw+WAAiKyiz59gPXrmz4+dappIuBp0xof79BBNnrqtgcekF9p/f2BwED5mwQnPXCRhs6NG8Arr5juf/yxhUJVVXImtcxMef+11+SngYiInN7mzZuRlJSETz/9FJGRkUhPT0dsbCyKiorgb+Fcf+jQIbz44otITU3Fs88+i02bNmHixInIy8vDoEGDHPAOJP2NW5j6xjHsXTsWjRo17jXw8ClBu843MHTUz3hq7H9hVLg/xg7pCmCsqVxFBZCTAzz7rG1BzJghx9107nzvskSkiA8/NDV0LKmokFtJieXj8+bJhs6FC/L3igkT5LCO4GD5X7uiQv4GUlYmZ4/r3Fl2r+vQwfx5amvlJAt37sjGlLNdSXKJrmvLlpkWX542DdiwoUGBGzfk9NH798vpo1evBhISlA+ciMjJOUvXtcjISAwfPhwf/+eXLoPBgNDQULz++uuYM2dOo/JxcXGoqKjAjh07jPtGjhyJ8PBwfPrpp/d8PXvm5dbtamz/xxms2VyCnPVjzY49/pscTJnohyF9/TDooQeh8Wpmiqbbt+VggIsXzfe7ucmuacHB8ke+3r0tP75XL9mtm4haldpaOb7H3182OMrLgVu3TFt5OfDzz8BPP8nlVbZutf212rWTi6XeuSMvCte/0BsaKrvQeXrKzcvL8t+WHNPpgOees33SlDbVdW3jRtPtJUsaHDx/Xl5+P3UK8PGR00fHxKgZHhERKai6uhq5ubmYO3eucZ+7uztiYmJw+PBhi485fPgwkpKSzPbFxsZi+/btFstXVVWhqm79AshK9n7MeGQtDh4bhKs1IbheGwSB/gD6G4+Par8HH/X8BEOLSoClLXjCqio57VNDW7YAv/rVfcVKRI7l4WEaadGundya6pQ0a5YcqXH6tFw5JTfXNOHBlSuAXm/qvarTya/Gnp6yIVVdLX8vqT9Ndn2XLsnNXvbtk1/LleT0DZ0rV+RQG0COzQkKalAgJUU2crp0kdNghIWpHiMRESnn+vXrqK2tRUCDMZcBAQE4deqUxcfo9XqL5fV6vcXyqampeOedd+wTMIBrVzT4vso0fkiH2xiKPIzGfkzBJoRV/gCcsPHJBw6UI5nbt7dPsETkVNzd5cXdfv1kh6b6amvlFaAOHUyLmQLyou+tW3KK67Iy2Zhq397012CQPWIrK2VXt5oaudXdbvi3uWNffCEbUzdvKp8Lp2/o/PCD6fb771so8NFH8l/nvfesmHOaiIjIZO7cuWZXgMrKyhBafz5ZK03+ox+G5f4F/YOBQSGeeMhPC3d3NwB9AdxHg2rkSDsuoENErsbDA+jYsfF+Nze5/pa3d9OPtXXYX0Nr1tjneVrC6Rs65eXy7/DhwEMP/Wfn3r3AY4/Jf7UOHYB16xwWHxERKcvPzw8eHh4oaTAqt6SkBIGBlmcpCwwMtKq8VquFVqu1T8AAnp/xC7s9FxERWeZkcyc0lpMj/w4YAHnl5q23gCeekFdwiIjI5Wk0GgwbNgzZ2dnGfQaDAdnZ2YiKarzWDABERUWZlQeAb7/9tsnyRETkfJz+ik7dVNKjR9wBprwEbN7s0HiIiEh9SUlJSEhIQEREBEaMGIH09HRUVFTg5f+sAzNt2jSEhIQgNTUVADBz5kyMGTMGH374IZ555hlkZmbi2LFjWLlypSPfBhER2ZFNV3QyMjLQvXt36HQ6REZG4mj95Vst2Lp1K/r16wedToewsDBkZWXZFGxDdVPedcINTPzkKdnI8fSU80svXGiX1yAiotYvLi4OaWlpWLRoEcLDw5Gfn4+dO3caJxy4ePEirl69aiwfHR2NTZs2YeXKlXj44Yfx5ZdfYvv27Q5dQ4eIiOzL6nV0Nm/ejGnTppktyrZ169ZmF2UbPXq02aJsS5cutWpRtqbmyl64EPi/987hGzyNfiiSc+Rt2ya7rhER0X1zlnV01Ma8EBE5TkvPwVY3dNRYlM3SegWhoaGN3szcmZVI/KgPuuAyp48mIlIAv9BbxrwQETlOS8/BVnVdq1uULabegpstWZQtpsECnbGxsU2WB+R6Bb6+vsatqSk873q1x0Kk4LJ/OHDkCBs5REREREQEwMqGTnOLsjW1yJq1i7IBcr2C0tJS43apiWVYX3gBiF75Mi5/9U+ukUNEREREREatcta1lq5XMHKk3ACN4jEREREREZHzsOqKjhqLshEREREREd0vqxo6XJSNiIiIiIicgdVd1xyxKFvdxHBlZWXWhktERPeh7rxr5QSdLo/1EhGR47S0brK6oRMXF4dr165h0aJF0Ov1CA8Pb7Qom7u76UJR3aJsCxYswLx589C7d2+rF2UrLy8HgCZnXyMiImWVl5fD19fX0WG0GqyXiIgc7151k9Xr6DiCwWDAlStX4O3tDTc3N+P+uvV1Ll26xHUMwHw0xHyYYz5MmAtzzeVDCIHy8nIEBweb/YjV1jVVL6mNn2WJeZCYBxPmQnLVPLS0bmqVs6415O7uji5dujR53MfHx6X+8e4X82GO+TDHfJgwF+aaygev5DR2r3pJbfwsS8yDxDyYMBeSK+ahJXUTf54jIiIiIiKXw4YOERERERG5HKdu6Gi1WiQnJ7docdG2gPkwx3yYYz5MmAtzzIfz4r+dxDxIzIMJcyG19Tw4xWQERERERERE1nDqKzpERERERESWsKFDREREREQuhw0dIiIiIiJyOWzoEBERERGRy2FDh4iIiIiIXE6rb+hkZGSge/fu0Ol0iIyMxNGjR5stv3XrVvTr1w86nQ5hYWHIyspSKVJ1WJOPVatW4dFHH0WnTp3QqVMnxMTE3DN/zsbaz0edzMxMuLm5YeLEicoGqDJr8/Hvf/8biYmJCAoKglarRZ8+fVzm/4y1uUhPT0ffvn3Rrl07hIaGYvbs2bhz545K0Spr//79GD9+PIKDg+Hm5obt27ff8zE5OTkYOnQotFotevXqhfXr1yseJ1nG875kTR62bduGiIgIdOzYER06dEB4eDg+//xzFaNVDus9E2tysX79eri5uZltOp1OxWiVw7q/GaIVy8zMFBqNRqxdu1YUFBSIV155RXTs2FGUlJRYLH/w4EHh4eEhli1bJgoLC8WCBQuEl5eXOHHihMqRK8PafEyZMkVkZGSI48ePi5MnT4qXXnpJ+Pr6ih9//FHlyJVhbT7qnDt3ToSEhIhHH31UTJgwQZ1gVWBtPqqqqkRERIQYN26cOHDggDh37pzIyckR+fn5Kkduf9bmYuPGjUKr1YqNGzeKc+fOiV27domgoCAxe/ZslSNXRlZWlpg/f77Ytm2bACC+/vrrZsufPXtWtG/fXiQlJYnCwkKxfPly4eHhIXbu3KlOwGTE875kbR727dsntm3bJgoLC0VxcbFIT093ic8w6z0Ta3Oxbt064ePjI65evWrc9Hq9ylHbH+v+5rXqhs6IESNEYmKi8X5tba0IDg4WqampFstPmjRJPPPMM2b7IiMjxfTp0xWNUy3W5qOhmpoa4e3tLTZs2KBUiKqyJR81NTUiOjparF69WiQkJLjMCV8I6/PxySefiB49eojq6mq1QlSNtblITEwUjz/+uNm+pKQkMWrUKEXjdISWNHTefPNNMXDgQLN9cXFxIjY2VsHIyBKe96X7zYMQQgwZMkQsWLBAifBUw3rPxNpcrFu3Tvj6+qoUnXpY9zev1XZdq66uRm5uLmJiYoz73N3dERMTg8OHD1t8zOHDh83KA0BsbGyT5Z2JLfloqLKyEnfv3sUDDzygVJiqsTUf7777Lvz9/fHb3/5WjTBVY0s+/vrXvyIqKgqJiYkICAjAoEGDsHjxYtTW1qoVtiJsyUV0dDRyc3ONl/vPnj2LrKwsjBs3TpWYWxtXPpc6E573pfvNgxAC2dnZKCoqwujRo5UMVVGs90xszcWtW7fQrVs3hIaGYsKECSgoKFAjXMWw7r83T0cH0JTr16+jtrYWAQEBZvsDAgJw6tQpi4/R6/UWy+v1esXiVIst+WjorbfeQnBwcKMvMM7IlnwcOHAAa9asQX5+vgoRqsuWfJw9exZ79+7F1KlTkZWVheLiYsyYMQN3795FcnKyGmErwpZcTJkyBdevX8cjjzwCIQRqamrw+9//HvPmzVMj5FanqXNpWVkZbt++jXbt2jkosraF533J1jyUlpYiJCQEVVVV8PDwwIoVK/Dkk08qHa5iWO+Z2JKLvn37Yu3atRg8eDBKS0uRlpaG6OhoFBQUoEuXLmqEbXes+++t1TZ0yL6WLFmCzMxM5OTkuMzgO2uUl5cjPj4eq1atgp+fn6PDaRUMBgP8/f2xcuVKeHh4YNiwYbh8+TI++OADlzzZNScnJweLFy/GihUrEBkZieLiYsycORMpKSlYuHCho8MjsklbP+97e3sjPz8ft27dQnZ2NpKSktCjRw+MHTvW0aGpgvWeuaioKERFRRnvR0dHo3///vjss8+QkpLiwMjU1dbq/lbb0PHz84OHhwdKSkrM9peUlCAwMNDiYwIDA60q70xsyUedtLQ0LFmyBHv27MHgwYOVDFM11ubjzJkzOH/+PMaPH2/cZzAYAACenp4oKipCz549lQ1aQbZ8PoKCguDl5QUPDw/jvv79+0Ov16O6uhoajUbRmJViSy4WLlyI+Ph4/O53vwMAhIWFoaKiAq+++irmz58Pd/dW28tXEU2dS318fHg1R0U870u25sHd3R29evUCAISHh+PkyZNITU112oYO6z2T+/m/UcfLywtDhgxBcXGxEiGqgnX/vbXa2luj0WDYsGHIzs427jMYDMjOzjZrkdcXFRVlVh4Avv322ybLOxNb8gEAy5YtQ0pKCnbu3ImIiAg1QlWFtfno168fTpw4gfz8fOP2y1/+Eo899hjy8/MRGhqqZvh2Z8vnY9SoUSguLjZWfABw+vRpBAUFOfWJzpZcVFZWNmrM1FUCQgjlgm2lXPlc6kx43pdszUNDBoMBVVVVSoSoCtZ7Jvb4TNTW1uLEiRMICgpSKkzFse5vAQdPhtCszMxModVqxfr160VhYaF49dVXRceOHY3TAcbHx4s5c+YYyx88eFB4enqKtLQ0cfLkSZGcnOxy00tbk48lS5YIjUYjvvzyS7PpFMvLyx31FuzK2nw05EqzzwhhfT4uXrwovL29xWuvvSaKiorEjh07hL+/v3jvvfcc9RbsxtpcJCcnC29vb/HFF1+Is2fPit27d4uePXuKSZMmOeot2FV5ebk4fvy4OH78uAAg/vSnP4njx4+LCxcuCCGEmDNnjoiPjzeWr5te+o033hAnT54UGRkZLjE1rzPieV+yNg+LFy8Wu3fvFmfOnBGFhYUiLS1NeHp6ilWrVjnqLdgF6z0Ta3PxzjvviF27dokzZ86I3NxcMXnyZKHT6URBQYGj3oJdsO5vXqtu6AghxPLly0XXrl2FRqMRI0aMEEeOHDEeGzNmjEhISDArv2XLFtGnTx+h0WjEwIEDxd///neVI1aWNfno1q2bANBoS05OVj9whVj7+ajPlU74dazNx6FDh0RkZKTQarWiR48e4v333xc1NTUqR60Ma3Jx9+5d8fbbb4uePXsKnU4nQkNDxYwZM8TNmzfVD1wB+/bts3guqMtBQkKCGDNmTKPHhIeHC41GI3r06CHWrVunetwk8bwvWZOH+fPni169egmdTic6deokoqKiRGZmpgOitj/WeybW5GLWrFnGsgEBAWLcuHEiLy/PAVHbH+v+prkJ0Qb7ZRARERERkUtrtWN0iIiIiIiIbMWGDhERERERuRw2dIiIiIiIyOWwoUNERERERC6HDR0iIiIiInI5bOgQEREREZHLYUOHiIiIiIhcDhs6RERERETkctjQISIiIiIil8OGDhERERERuRw2dIiIiIiIyOX8PzQ5kD53/Y2ZAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rate=0.15\n",
    "n_estimators=30\n",
    "max_depth= 2\n",
    "num_leaves=3\n",
    "lambda_l1=0\n",
    "lambda_l2=10\n",
    "\n",
    "\n",
    "x_train = train[feature_select_result ]\n",
    "y_train = train['def_pd1']\n",
    "x_test = test[feature_select_result]\n",
    "y_test = test['def_pd1']\n",
    "from lightgbm import LGBMClassifier\n",
    "import IPython.display as display\n",
    "\n",
    "\n",
    "lgb1 = LGBMClassifier(learning_rate= learning_rate,n_estimators= n_estimators,\n",
    "                        max_depth=max_depth,num_leaves=num_leaves,\n",
    "                        subsample=0.8,colsample_bytree=0.8,verbose= -1,\n",
    "                        boosting_type='gbdt',min_data_in_leaf=100,\n",
    "                        lambda_l2=lambda_l2,lambda_l1=lambda_l1,feature_fraction=0.15,\n",
    "                        seed =40,bagging_seed=40,feature_fraction_seed=40,eval_metric='',max_bin=10\n",
    "                        )\n",
    "lgb1.fit(train[feature_select_result],train['def_pd1'])\n",
    "# lgb1.fit(x_train,y_train) #,early_stopping_rounds=20 ,eval_set=[(x_test,y_test)],early_stopping_rounds=40\n",
    "# lgb1.fit(x_train,y_train,eval_set=[(x_test,y_test)],early_stopping_rounds=30)\n",
    "train['y_p'] = lgb1.predict_proba(train[feature_select_result])[:,1]\n",
    "test['y_p'] = lgb1.predict_proba(test[feature_select_result])[:,1]\n",
    "oot['y_p'] = lgb1.predict_proba(oot[feature_select_result])[:,1]\n",
    "oot2['y_p'] = lgb1.predict_proba(oot2[feature_select_result])[:,1]\n",
    "\n",
    "psi = fea_psi_calc(oot['y_p'],oot2['y_p'])['psi']\n",
    "ks1 = calc_ks(train['def_pd1'],train['y_p'])\n",
    "ks2 = calc_ks(test['def_pd1'],test['y_p'])\n",
    "ks3 = calc_ks(oot['def_pd1'],oot['y_p'])\n",
    "\n",
    "display.clear_output(wait=False)\n",
    "\n",
    "print(psi)\n",
    "plot_roc_ks(train['y_p'],train['def_pd1'],'train')\n",
    "plot_roc_ks(test['y_p'],test['def_pd1'],'test')\n",
    "plot_roc_ks(oot['y_p'],oot['def_pd1'],'oot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:41:46.144524Z",
     "start_time": "2024-03-28T12:41:46.137561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                        var  importance\n0              L4_r_level_app_package_ratio           8\n2                    loanBillsLoanAvgAmount           7\n4              L2_r_level_app_package_ratio           7\n6    financial_non_system_app_package_ratio           6\n3              L1_r_level_app_package_ratio           5\n10                 last1LoanBillsLoanAmount           5\n8              non_system_app_package_ratio           4\n13                    lastAllAvgOverdueDays           4\n14                     lastEndLoanDeltaDays           4\n12               bank_sub_app_package_ratio           3\n5                   last1EndBillsLoanAmount           2\n9   wallet_sub_non_system_app_package_ratio           2\n1              L3_r_level_app_package_ratio           1\n11                 system_app_package_count           1\n15         creditcard_sub_app_package_ratio           1\n7               financial_app_package_ratio           0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>importance</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>L4_r_level_app_package_ratio</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>loanBillsLoanAvgAmount</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>L2_r_level_app_package_ratio</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>financial_non_system_app_package_ratio</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>L1_r_level_app_package_ratio</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>last1LoanBillsLoanAmount</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>non_system_app_package_ratio</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>lastAllAvgOverdueDays</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>lastEndLoanDeltaDays</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>bank_sub_app_package_ratio</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>last1EndBillsLoanAmount</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>wallet_sub_non_system_app_package_ratio</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>L3_r_level_app_package_ratio</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>system_app_package_count</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>creditcard_sub_app_package_ratio</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>financial_app_package_ratio</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fea_info=pd.DataFrame({'var':feature_select_result,'importance':lgb1.feature_importances_})\\\n",
    "        .sort_values('importance',ascending=False)\n",
    "fea_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第14175轮，耗时0.12413430213928223,0.4474842395915837,0.30086485131451135,0.4608264912868633,0.03042427499619797\n",
      "14175\n"
     ]
    }
   ],
   "source": [
    "n =0\n",
    "arr = []\n",
    "import warnings \n",
    "\n",
    "x_train = train[feature_select_result ]\n",
    "y_train = train['def_pd1']\n",
    "x_test = test[feature_select_result]\n",
    "y_test = test['def_pd1']\n",
    "from lightgbm import LGBMClassifier\n",
    "for learning_rate in  np.arange(0.05,0.4,0.05) : \n",
    "    for n_estimators in np.arange(10,100,10) : # np.arange(10,150,10) [20,30,50,70,90,120,130]\n",
    "        for max_depth in  [2,3,4] :#np.arange(1,6,1):\n",
    "            for num_leaves in [3,5,9] :#[3,5,7,9,11,13,15]: 13,15\n",
    "                for lambda_l1 in [0, 0.1, 0.2,0.4, 0.6]:# [0, 0.1, 0.4,  0.6]:  #[0, 0.1, 0.4, 0.5, 0.6]\n",
    "                    for lambda_l2 in [0, 10, 15,20,25]: #[0, 10, 15, 35, 40 ] # [0 ,10, 15, 35, 40]: #[0, 10, 15, 35, 40]\n",
    "                        start_time= time.time()\n",
    "                        lgb1 = LGBMClassifier(learning_rate= learning_rate,n_estimators= n_estimators,\n",
    "                        max_depth=max_depth,num_leaves=num_leaves,\n",
    "                        subsample=0.8,colsample_bytree=0.8,verbose= -1,\n",
    "                        boosting_type='gbdt',min_data_in_leaf=100,\n",
    "                        lambda_l2=lambda_l2,lambda_l1=lambda_l1,\n",
    "                        seed =40,bagging_seed=40,feature_fraction_seed=40,eval_metric='',max_bin=10\n",
    "                                             )\n",
    "                        lgb1.fit(x_train,y_train) #,eval_set=[(x_test,y_test)],early_stopping_rounds=40\n",
    "\n",
    "                        train['y_p'] = lgb1.predict_proba(train[feature_select_result])[:,1]\n",
    "                        test['y_p'] = lgb1.predict_proba(test[feature_select_result])[:,1]\n",
    "                        oot['y_p'] = lgb1.predict_proba(oot[feature_select_result])[:,1]\n",
    "                        oot2['y_p'] = lgb1.predict_proba(oot2[feature_select_result])[:,1]\n",
    "\n",
    "\n",
    "                        psi = fea_psi_calc(oot['y_p'],oot2['y_p'])['psi']\n",
    "\n",
    "                        ks1 = calc_ks(train['def_pd1'],train['y_p'])\n",
    "                        ks2 = calc_ks(test['def_pd1'],test['y_p'])\n",
    "                        ks3 = calc_ks(oot['def_pd1'],oot['y_p'])\n",
    "\n",
    "                        arr.append([learning_rate,n_estimators,max_depth,num_leaves,\n",
    "                                    lambda_l1,lambda_l2,ks1,ks2,ks3,ks1-ks2,psi])\n",
    "                        end_time = time.time()\n",
    "                        n=n+1\n",
    "\n",
    "                        display.clear_output(wait=False)\n",
    "\n",
    "                        print(f\"第{n}轮，耗时{end_time-start_time},{ks1},{ks2},{ks3},{psi}\")\n",
    "                        \n",
    "print(n)\n",
    "\n",
    "rs = pd.DataFrame(arr,columns=['learning_rate','n_estimators','max_depth','num_leaves','lambda_l1','lambda_l2',\n",
    "                              'ks1','ks2','ks3','delt','psi'])\n",
    "# params_path = os.path.join(work_dir,'newcust_params240316v2.xlsx')\n",
    "rs.to_excel('params240327v1.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型持久化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lightgbm.basic.Booster at 0x7f409866f950>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lgb1.booster_.save_model('amios_oldcust_score240327.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:00.767234Z",
     "start_time": "2024-03-30T07:05:00.742756Z"
    }
   },
   "outputs": [],
   "source": [
    "gbm_new = lgb.Booster(model_file='amios_oldcust_score240327.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:03.557011Z",
     "start_time": "2024-03-30T07:05:03.365478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         var   target          bin  negative  positive  total  total_rate  \\\n0  score_bin  def_pd1     (0, 530]        94       249    343    0.010184   \n1  score_bin  def_pd1   (530, 550]       418       838   1256    0.037293   \n2  score_bin  def_pd1   (550, 570]      1508      2209   3717    0.110366   \n3  score_bin  def_pd1   (570, 590]      3955      3831   7786    0.231183   \n4  score_bin  def_pd1   (590, 610]      4872      3113   7985    0.237091   \n5  score_bin  def_pd1   (610, 630]      4549      1671   6220    0.184685   \n6  score_bin  def_pd1   (630, 650]      3190       744   3934    0.116809   \n7  score_bin  def_pd1  (650, 1000]      2230       208   2438    0.072389   \n\n   positive_rate       woe      LIFT        KS        IV  \n0       0.725948 -1.305858  1.900738  0.274992  0.509509  \n1       0.667197 -1.143538  1.746913  0.274992  0.509509  \n2       0.594296 -0.855219  1.556038  0.274992  0.509509  \n3       0.492037 -0.447615  1.288293  0.274992  0.509509  \n4       0.389856 -0.033309  1.020754  0.274992  0.509509  \n5       0.268650  0.517015  0.703401  0.274992  0.509509  \n6       0.189120  0.963731  0.495171  0.274992  0.509509  \n7       0.085316  1.840139  0.223381  0.274992  0.509509  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>target</th>\n      <th>bin</th>\n      <th>negative</th>\n      <th>positive</th>\n      <th>total</th>\n      <th>total_rate</th>\n      <th>positive_rate</th>\n      <th>woe</th>\n      <th>LIFT</th>\n      <th>KS</th>\n      <th>IV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(0, 530]</td>\n      <td>94</td>\n      <td>249</td>\n      <td>343</td>\n      <td>0.010184</td>\n      <td>0.725948</td>\n      <td>-1.305858</td>\n      <td>1.900738</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(530, 550]</td>\n      <td>418</td>\n      <td>838</td>\n      <td>1256</td>\n      <td>0.037293</td>\n      <td>0.667197</td>\n      <td>-1.143538</td>\n      <td>1.746913</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(550, 570]</td>\n      <td>1508</td>\n      <td>2209</td>\n      <td>3717</td>\n      <td>0.110366</td>\n      <td>0.594296</td>\n      <td>-0.855219</td>\n      <td>1.556038</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(570, 590]</td>\n      <td>3955</td>\n      <td>3831</td>\n      <td>7786</td>\n      <td>0.231183</td>\n      <td>0.492037</td>\n      <td>-0.447615</td>\n      <td>1.288293</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(590, 610]</td>\n      <td>4872</td>\n      <td>3113</td>\n      <td>7985</td>\n      <td>0.237091</td>\n      <td>0.389856</td>\n      <td>-0.033309</td>\n      <td>1.020754</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(610, 630]</td>\n      <td>4549</td>\n      <td>1671</td>\n      <td>6220</td>\n      <td>0.184685</td>\n      <td>0.268650</td>\n      <td>0.517015</td>\n      <td>0.703401</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(630, 650]</td>\n      <td>3190</td>\n      <td>744</td>\n      <td>3934</td>\n      <td>0.116809</td>\n      <td>0.189120</td>\n      <td>0.963731</td>\n      <td>0.495171</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(650, 1000]</td>\n      <td>2230</td>\n      <td>208</td>\n      <td>2438</td>\n      <td>0.072389</td>\n      <td>0.085316</td>\n      <td>1.840139</td>\n      <td>0.223381</td>\n      <td>0.274992</td>\n      <td>0.509509</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gbm_new = lgb.Booster(model_file=model_txt_path)\n",
    "data_df['y_p'] = lgb1.predict_proba(data_df[feature_select_result])[:,1] # type: ignore\n",
    "data_df['y_p_txt'] = gbm_new.predict(data_df[feature_select_result])\n",
    "data_df['score_txt'] = data_df['y_p_txt'].map(lambda x: round(550 - 60 / np.log(2) * np.log(x / (1 - x)), 0))\n",
    "data_df['score_bin'] = pd.cut(data_df['score_txt'], [0,530,550,570,590,610,630,650,1000])\n",
    "univerate(data_df, 'score_bin', 'def_pd1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:10.067762Z",
     "start_time": "2024-03-30T07:05:09.999217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         var   target          bin  negative  positive  total  total_rate  \\\n0  score_bin  def_pd1     (0, 530]        22        68     90    0.012159   \n1  score_bin  def_pd1   (530, 550]        99       214    313    0.042286   \n2  score_bin  def_pd1   (550, 570]       329       497    826    0.111591   \n3  score_bin  def_pd1   (570, 590]       898       808   1706    0.230478   \n4  score_bin  def_pd1   (590, 610]      1090       624   1714    0.231559   \n5  score_bin  def_pd1   (610, 630]      1031       325   1356    0.183194   \n6  score_bin  def_pd1   (630, 650]       721       143    864    0.116725   \n7  score_bin  def_pd1  (650, 1000]       495        38    533    0.072008   \n\n   positive_rate       woe      LIFT        KS        IV  \n0       0.755556 -1.519422  2.058381  0.296373  0.606129  \n1       0.683706 -1.282072  1.862640  0.296373  0.606129  \n2       0.601695 -0.948682  1.639214  0.296373  0.606129  \n3       0.473623 -0.437383  1.290303  0.296373  0.606129  \n4       0.364061  0.012889  0.991821  0.296373  0.606129  \n5       0.239676  0.605830  0.652955  0.296373  0.606129  \n6       0.165509  1.060612  0.450902  0.296373  0.606129  \n7       0.071295  1.962495  0.194230  0.296373  0.606129  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>target</th>\n      <th>bin</th>\n      <th>negative</th>\n      <th>positive</th>\n      <th>total</th>\n      <th>total_rate</th>\n      <th>positive_rate</th>\n      <th>woe</th>\n      <th>LIFT</th>\n      <th>KS</th>\n      <th>IV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(0, 530]</td>\n      <td>22</td>\n      <td>68</td>\n      <td>90</td>\n      <td>0.012159</td>\n      <td>0.755556</td>\n      <td>-1.519422</td>\n      <td>2.058381</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(530, 550]</td>\n      <td>99</td>\n      <td>214</td>\n      <td>313</td>\n      <td>0.042286</td>\n      <td>0.683706</td>\n      <td>-1.282072</td>\n      <td>1.862640</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(550, 570]</td>\n      <td>329</td>\n      <td>497</td>\n      <td>826</td>\n      <td>0.111591</td>\n      <td>0.601695</td>\n      <td>-0.948682</td>\n      <td>1.639214</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(570, 590]</td>\n      <td>898</td>\n      <td>808</td>\n      <td>1706</td>\n      <td>0.230478</td>\n      <td>0.473623</td>\n      <td>-0.437383</td>\n      <td>1.290303</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(590, 610]</td>\n      <td>1090</td>\n      <td>624</td>\n      <td>1714</td>\n      <td>0.231559</td>\n      <td>0.364061</td>\n      <td>0.012889</td>\n      <td>0.991821</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(610, 630]</td>\n      <td>1031</td>\n      <td>325</td>\n      <td>1356</td>\n      <td>0.183194</td>\n      <td>0.239676</td>\n      <td>0.605830</td>\n      <td>0.652955</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(630, 650]</td>\n      <td>721</td>\n      <td>143</td>\n      <td>864</td>\n      <td>0.116725</td>\n      <td>0.165509</td>\n      <td>1.060612</td>\n      <td>0.450902</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(650, 1000]</td>\n      <td>495</td>\n      <td>38</td>\n      <td>533</td>\n      <td>0.072008</td>\n      <td>0.071295</td>\n      <td>1.962495</td>\n      <td>0.194230</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_new['y_p'] = lgb1.predict_proba(data_df_new[feature_select_result])[:,1] # type: ignore\n",
    "data_df_new['y_p_txt'] = gbm_new.predict(data_df_new[feature_select_result])\n",
    "data_df_new['score_txt'] = data_df_new['y_p_txt'].map(lambda x: round(550 - 60 / np.log(2) * np.log(x / (1 - x)), 0))\n",
    "data_df_new['score_bin'] = pd.cut(data_df_new['score_txt'], [0,530,550,570,590,610,630,650,1000])\n",
    "univerate(data_df_new, 'score_bin', 'def_pd1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:13.041460Z",
     "start_time": "2024-03-30T07:05:12.987323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         var   target          bin  negative  positive  total  total_rate  \\\n0  score_bin  def_pd1     (0, 530]        16        44     60    0.011477   \n1  score_bin  def_pd1   (530, 550]        65       150    215    0.041125   \n2  score_bin  def_pd1   (550, 570]       242       357    599    0.114575   \n3  score_bin  def_pd1   (570, 590]       639       554   1193    0.228194   \n4  score_bin  def_pd1   (590, 610]       772       429   1201    0.229725   \n5  score_bin  def_pd1   (610, 630]       716       219    935    0.178845   \n6  score_bin  def_pd1   (630, 650]       548        98    646    0.123565   \n7  score_bin  def_pd1  (650, 1000]       355        24    379    0.072494   \n\n   positive_rate       woe      LIFT        KS        IV  \n0       0.733333 -1.444320  2.044729  0.302426  0.643945  \n1       0.697674 -1.379619  1.945302  0.302426  0.643945  \n2       0.595993 -0.961523  1.661788  0.302426  0.643945  \n3       0.464376 -0.436652  1.294803  0.302426  0.643945  \n4       0.357202  0.006254  0.995975  0.302426  0.643945  \n5       0.234225  0.599508  0.653081  0.302426  0.643945  \n6       0.151703  1.127209  0.422988  0.302426  0.643945  \n7       0.063325  2.046994  0.176566  0.302426  0.643945  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>target</th>\n      <th>bin</th>\n      <th>negative</th>\n      <th>positive</th>\n      <th>total</th>\n      <th>total_rate</th>\n      <th>positive_rate</th>\n      <th>woe</th>\n      <th>LIFT</th>\n      <th>KS</th>\n      <th>IV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(0, 530]</td>\n      <td>16</td>\n      <td>44</td>\n      <td>60</td>\n      <td>0.011477</td>\n      <td>0.733333</td>\n      <td>-1.444320</td>\n      <td>2.044729</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(530, 550]</td>\n      <td>65</td>\n      <td>150</td>\n      <td>215</td>\n      <td>0.041125</td>\n      <td>0.697674</td>\n      <td>-1.379619</td>\n      <td>1.945302</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(550, 570]</td>\n      <td>242</td>\n      <td>357</td>\n      <td>599</td>\n      <td>0.114575</td>\n      <td>0.595993</td>\n      <td>-0.961523</td>\n      <td>1.661788</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(570, 590]</td>\n      <td>639</td>\n      <td>554</td>\n      <td>1193</td>\n      <td>0.228194</td>\n      <td>0.464376</td>\n      <td>-0.436652</td>\n      <td>1.294803</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(590, 610]</td>\n      <td>772</td>\n      <td>429</td>\n      <td>1201</td>\n      <td>0.229725</td>\n      <td>0.357202</td>\n      <td>0.006254</td>\n      <td>0.995975</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(610, 630]</td>\n      <td>716</td>\n      <td>219</td>\n      <td>935</td>\n      <td>0.178845</td>\n      <td>0.234225</td>\n      <td>0.599508</td>\n      <td>0.653081</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(630, 650]</td>\n      <td>548</td>\n      <td>98</td>\n      <td>646</td>\n      <td>0.123565</td>\n      <td>0.151703</td>\n      <td>1.127209</td>\n      <td>0.422988</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(650, 1000]</td>\n      <td>355</td>\n      <td>24</td>\n      <td>379</td>\n      <td>0.072494</td>\n      <td>0.063325</td>\n      <td>2.046994</td>\n      <td>0.176566</td>\n      <td>0.302426</td>\n      <td>0.643945</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['y_p_txt'] = gbm_new.predict(train[feature_select_result])\n",
    "train['score_txt'] = train['y_p_txt'].map(lambda x: round(550 - 60 / np.log(2) * np.log(x / (1 - x)), 0))\n",
    "train['score_bin'] = pd.cut(train['score_txt'], [0,530,550,570,590,610,630,650,1000])\n",
    "univerate(train, 'score_bin', 'def_pd1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:15.376515Z",
     "start_time": "2024-03-30T07:05:15.326038Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         var   target          bin  negative  positive  total  total_rate  \\\n0  score_bin  def_pd1     (0, 530]         6        24     30    0.013799   \n1  score_bin  def_pd1   (530, 550]        34        64     98    0.045078   \n2  score_bin  def_pd1   (550, 570]        87       140    227    0.104416   \n3  score_bin  def_pd1   (570, 590]       259       254    513    0.235971   \n4  score_bin  def_pd1   (590, 610]       318       195    513    0.235971   \n5  score_bin  def_pd1   (610, 630]       315       106    421    0.193652   \n6  score_bin  def_pd1   (630, 650]       173        45    218    0.100276   \n7  score_bin  def_pd1  (650, 1000]       140        14    154    0.070837   \n\n   positive_rate       woe      LIFT        KS       IV  \n0       0.800000 -1.678944  2.065558  0.282657  0.52979  \n1       0.653061 -1.065821  1.686170  0.282657  0.52979  \n2       0.616740 -0.925193  1.592391  0.282657  0.52979  \n3       0.495127 -0.437343  1.278391  0.282657  0.52979  \n4       0.380117  0.030266  0.981442  0.282657  0.52979  \n5       0.251781  0.626784  0.650087  0.282657  0.52979  \n6       0.206422  0.877104  0.532971  0.282657  0.52979  \n7       0.090909  1.794994  0.234723  0.282657  0.52979  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>target</th>\n      <th>bin</th>\n      <th>negative</th>\n      <th>positive</th>\n      <th>total</th>\n      <th>total_rate</th>\n      <th>positive_rate</th>\n      <th>woe</th>\n      <th>LIFT</th>\n      <th>KS</th>\n      <th>IV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(0, 530]</td>\n      <td>6</td>\n      <td>24</td>\n      <td>30</td>\n      <td>0.013799</td>\n      <td>0.800000</td>\n      <td>-1.678944</td>\n      <td>2.065558</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(530, 550]</td>\n      <td>34</td>\n      <td>64</td>\n      <td>98</td>\n      <td>0.045078</td>\n      <td>0.653061</td>\n      <td>-1.065821</td>\n      <td>1.686170</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(550, 570]</td>\n      <td>87</td>\n      <td>140</td>\n      <td>227</td>\n      <td>0.104416</td>\n      <td>0.616740</td>\n      <td>-0.925193</td>\n      <td>1.592391</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(570, 590]</td>\n      <td>259</td>\n      <td>254</td>\n      <td>513</td>\n      <td>0.235971</td>\n      <td>0.495127</td>\n      <td>-0.437343</td>\n      <td>1.278391</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(590, 610]</td>\n      <td>318</td>\n      <td>195</td>\n      <td>513</td>\n      <td>0.235971</td>\n      <td>0.380117</td>\n      <td>0.030266</td>\n      <td>0.981442</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(610, 630]</td>\n      <td>315</td>\n      <td>106</td>\n      <td>421</td>\n      <td>0.193652</td>\n      <td>0.251781</td>\n      <td>0.626784</td>\n      <td>0.650087</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(630, 650]</td>\n      <td>173</td>\n      <td>45</td>\n      <td>218</td>\n      <td>0.100276</td>\n      <td>0.206422</td>\n      <td>0.877104</td>\n      <td>0.532971</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(650, 1000]</td>\n      <td>140</td>\n      <td>14</td>\n      <td>154</td>\n      <td>0.070837</td>\n      <td>0.090909</td>\n      <td>1.794994</td>\n      <td>0.234723</td>\n      <td>0.282657</td>\n      <td>0.52979</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['y_p_txt'] = gbm_new.predict(test[feature_select_result])\n",
    "test['score_txt'] = test['y_p_txt'].map(lambda x: round(550 - 60 / np.log(2) * np.log(x / (1 - x)), 0))\n",
    "test['score_bin'] = pd.cut(test['score_txt'], [0,530,550,570,590,610,630,650,1000])\n",
    "univerate(test, 'score_bin', 'def_pd1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-30T07:05:17.688510Z",
     "start_time": "2024-03-30T07:05:17.610384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "            loan_week    score_bin  def_pd1  agr_pd1  def_cpd  agr_cpd  \\\n0    2023.10.30~11.05     (0, 530]      0.0      0.0      0.0      0.0   \n1    2023.10.30~11.05   (530, 550]      0.0      0.0      0.0      0.0   \n2    2023.10.30~11.05   (550, 570]      5.0      9.0      5.0      9.0   \n3    2023.10.30~11.05   (570, 590]      6.0      8.0      6.0      8.0   \n4    2023.10.30~11.05   (590, 610]      1.0      7.0      1.0      7.0   \n5    2023.10.30~11.05   (610, 630]      0.0      3.0      0.0      3.0   \n6    2023.10.30~11.05   (630, 650]      0.0      0.0      0.0      0.0   \n7    2023.10.30~11.05  (650, 1000]      0.0      0.0      0.0      0.0   \n8    2023.11.06~11.12     (0, 530]      1.0      1.0      1.0      1.0   \n9    2023.11.06~11.12   (530, 550]      5.0      9.0      5.0      9.0   \n10   2023.11.06~11.12   (550, 570]      7.0     11.0      7.0     11.0   \n11   2023.11.06~11.12   (570, 590]     13.0     24.0     10.0     24.0   \n12   2023.11.06~11.12   (590, 610]     24.0     77.0     22.0     77.0   \n13   2023.11.06~11.12   (610, 630]     16.0    100.0     16.0    100.0   \n14   2023.11.06~11.12   (630, 650]      5.0     37.0      5.0     37.0   \n15   2023.11.06~11.12  (650, 1000]      2.0     45.0      2.0     45.0   \n16   2023.11.13~11.19     (0, 530]      4.0      5.0      4.0      5.0   \n17   2023.11.13~11.19   (530, 550]      9.0     13.0      9.0     13.0   \n18   2023.11.13~11.19   (550, 570]     40.0     70.0     34.0     70.0   \n19   2023.11.13~11.19   (570, 590]     52.0    145.0     50.0    145.0   \n20   2023.11.13~11.19   (590, 610]     77.0    189.0     72.0    189.0   \n21   2023.11.13~11.19   (610, 630]     50.0    237.0     49.0    237.0   \n22   2023.11.13~11.19   (630, 650]     22.0    106.0     21.0    106.0   \n23   2023.11.13~11.19  (650, 1000]     11.0    107.0      9.0    107.0   \n24   2023.11.20~11.26     (0, 530]      0.0      5.0      0.0      5.0   \n25   2023.11.20~11.26   (530, 550]     23.0     41.0     21.0     41.0   \n26   2023.11.20~11.26   (550, 570]     74.0    140.0     70.0    140.0   \n27   2023.11.20~11.26   (570, 590]    166.0    321.0    161.0    321.0   \n28   2023.11.20~11.26   (590, 610]    140.0    409.0    130.0    409.0   \n29   2023.11.20~11.26   (610, 630]     53.0    278.0     43.0    278.0   \n30   2023.11.20~11.26   (630, 650]     16.0    153.0     14.0    153.0   \n31   2023.11.20~11.26  (650, 1000]      6.0    116.0      6.0    116.0   \n32   2023.11.27~12.03     (0, 530]     17.0     23.0     13.0     23.0   \n33   2023.11.27~12.03   (530, 550]     45.0     71.0     40.0     71.0   \n34   2023.11.27~12.03   (550, 570]    123.0    235.0    110.0    235.0   \n35   2023.11.27~12.03   (570, 590]    275.0    588.0    260.0    588.0   \n36   2023.11.27~12.03   (590, 610]    215.0    552.0    207.0    552.0   \n37   2023.11.27~12.03   (610, 630]     97.0    393.0     84.0    393.0   \n38   2023.11.27~12.03   (630, 650]     27.0    252.0     24.0    252.0   \n39   2023.11.27~12.03  (650, 1000]     11.0    125.0      8.0    125.0   \n40   2023.12.04~12.10     (0, 530]      9.0     19.0      9.0     19.0   \n41   2023.12.04~12.10   (530, 550]     37.0     74.0     34.0     74.0   \n42   2023.12.04~12.10   (550, 570]    207.0    312.0    192.0    312.0   \n43   2023.12.04~12.10   (570, 590]    315.0    591.0    295.0    591.0   \n44   2023.12.04~12.10   (590, 610]    268.0    586.0    252.0    586.0   \n45   2023.12.04~12.10   (610, 630]    133.0    488.0    116.0    488.0   \n46   2023.12.04~12.10   (630, 650]     58.0    263.0     51.0    263.0   \n47   2023.12.04~12.10  (650, 1000]     16.0    140.0     15.0    140.0   \n48   2023.12.11~12.17     (0, 530]      8.0     18.0      8.0     18.0   \n49   2023.12.11~12.17   (530, 550]     39.0     58.0     37.0     58.0   \n50   2023.12.11~12.17   (550, 570]     90.0    155.0     87.0    155.0   \n51   2023.12.11~12.17   (570, 590]    171.0    343.0    163.0    343.0   \n52   2023.12.11~12.17   (590, 610]    119.0    361.0    114.0    361.0   \n53   2023.12.11~12.17   (610, 630]     66.0    299.0     56.0    299.0   \n54   2023.12.11~12.17   (630, 650]     63.0    185.0     59.0    185.0   \n55   2023.12.11~12.17  (650, 1000]     15.0    115.0     13.0    115.0   \n56   2023.12.18~12.24     (0, 530]     50.0     56.0     49.0     56.0   \n57   2023.12.18~12.24   (530, 550]     53.0     82.0     50.0     82.0   \n58   2023.12.18~12.24   (550, 570]    111.0    150.0     97.0    150.0   \n59   2023.12.18~12.24   (570, 590]    159.0    327.0    150.0    327.0   \n60   2023.12.18~12.24   (590, 610]    112.0    359.0    103.0    359.0   \n61   2023.12.18~12.24   (610, 630]     57.0    276.0     53.0    276.0   \n62   2023.12.18~12.24   (630, 650]     51.0    139.0     47.0    139.0   \n63   2023.12.18~12.24  (650, 1000]      5.0     65.0      4.0     65.0   \n64   2023.12.25~12.31     (0, 530]     34.0     36.0     32.0     36.0   \n65   2023.12.25~12.31   (530, 550]     45.0     62.0     31.0     62.0   \n66   2023.12.25~12.31   (550, 570]    104.0    154.0     97.0    154.0   \n67   2023.12.25~12.31   (570, 590]    204.0    399.0    192.0    399.0   \n68   2023.12.25~12.31   (590, 610]    157.0    464.0    146.0    464.0   \n69   2023.12.25~12.31   (610, 630]     86.0    324.0     76.0    324.0   \n70   2023.12.25~12.31   (630, 650]     21.0    157.0     18.0    157.0   \n71   2023.12.25~12.31  (650, 1000]      1.0     82.0      0.0     82.0   \n72   2024.01.01~01.07     (0, 530]      4.0      4.0      3.0      4.0   \n73   2024.01.01~01.07   (530, 550]     38.0     54.0     35.0     54.0   \n74   2024.01.01~01.07   (550, 570]    108.0    179.0    105.0    179.0   \n75   2024.01.01~01.07   (570, 590]    219.0    451.0    209.0    451.0   \n76   2024.01.01~01.07   (590, 610]    102.0    386.0     87.0    386.0   \n77   2024.01.01~01.07   (610, 630]     84.0    370.0     79.0    370.0   \n78   2024.01.01~01.07   (630, 650]     32.0    171.0     26.0    171.0   \n79   2024.01.01~01.07  (650, 1000]      7.0    123.0      6.0    123.0   \n80   2024.01.08~01.14     (0, 530]      2.0      4.0      2.0      4.0   \n81   2024.01.08~01.14   (530, 550]     42.0     54.0     40.0     54.0   \n82   2024.01.08~01.14   (550, 570]    127.0    224.0    119.0    224.0   \n83   2024.01.08~01.14   (570, 590]    154.0    393.0    140.0    393.0   \n84   2024.01.08~01.14   (590, 610]    164.0    430.0    147.0    430.0   \n85   2024.01.08~01.14   (610, 630]     73.0    319.0     65.0    319.0   \n86   2024.01.08~01.14   (630, 650]     26.0    192.0     23.0    192.0   \n87   2024.01.08~01.14  (650, 1000]     10.0    107.0      7.0    107.0   \n88   2024.01.15~01.21     (0, 530]      8.0     12.0      8.0     12.0   \n89   2024.01.15~01.21   (530, 550]     59.0     92.0     59.0     92.0   \n90   2024.01.15~01.21   (550, 570]    133.0    231.0    129.0    231.0   \n91   2024.01.15~01.21   (570, 590]    211.0    483.0    200.0    483.0   \n92   2024.01.15~01.21   (590, 610]    179.0    455.0    158.0    455.0   \n93   2024.01.15~01.21   (610, 630]     95.0    337.0     87.0    337.0   \n94   2024.01.15~01.21   (630, 650]     37.0    209.0     28.0    209.0   \n95   2024.01.15~01.21  (650, 1000]     18.0    108.0     14.0    108.0   \n96   2024.01.22~01.28     (0, 530]     11.0     12.0     11.0     12.0   \n97   2024.01.22~01.28   (530, 550]     44.0     81.0     43.0     81.0   \n98   2024.01.22~01.28   (550, 570]    118.0    224.0    105.0    224.0   \n99   2024.01.22~01.28   (570, 590]    193.0    435.0    168.0    435.0   \n100  2024.01.22~01.28   (590, 610]    157.0    435.0    127.0    435.0   \n101  2024.01.22~01.28   (610, 630]     85.0    299.0     79.0    299.0   \n102  2024.01.22~01.28   (630, 650]     54.0    196.0     44.0    196.0   \n103  2024.01.22~01.28  (650, 1000]     12.0    101.0     10.0    101.0   \n104  2024.01.29~02.04     (0, 530]     15.0     31.0     15.0     31.0   \n105  2024.01.29~02.04   (530, 550]     57.0    102.0     52.0    102.0   \n106  2024.01.29~02.04   (550, 570]    142.0    273.0    137.0    273.0   \n107  2024.01.29~02.04   (570, 590]    254.0    555.0    225.0    555.0   \n108  2024.01.29~02.04   (590, 610]    181.0    477.0    153.0    477.0   \n109  2024.01.29~02.04   (610, 630]     96.0    341.0     84.0    341.0   \n110  2024.01.29~02.04   (630, 650]     59.0    238.0     55.0    238.0   \n111  2024.01.29~02.04  (650, 1000]      5.0     93.0      2.0     93.0   \n112  2024.02.05~02.11     (0, 530]     32.0     33.0     31.0     33.0   \n113  2024.02.05~02.11   (530, 550]     50.0     61.0     50.0     61.0   \n114  2024.02.05~02.11   (550, 570]    109.0    200.0    108.0    200.0   \n115  2024.02.05~02.11   (570, 590]    180.0    314.0    160.0    314.0   \n116  2024.02.05~02.11   (590, 610]    196.0    387.0    180.0    387.0   \n117  2024.02.05~02.11   (610, 630]    110.0    283.0     80.0    283.0   \n118  2024.02.05~02.11   (630, 650]     18.0    176.0     16.0    176.0   \n119  2024.02.05~02.11  (650, 1000]     10.0    107.0      8.0    107.0   \n120  2024.02.12~02.18     (0, 530]      7.0      7.0      7.0      7.0   \n121  2024.02.12~02.18   (530, 550]     39.0     52.0     36.0     52.0   \n122  2024.02.12~02.18   (550, 570]    141.0    195.0    124.0    195.0   \n123  2024.02.12~02.18   (570, 590]    166.0    308.0    156.0    308.0   \n124  2024.02.12~02.18   (590, 610]    195.0    345.0    168.0    345.0   \n125  2024.02.12~02.18   (610, 630]     93.0    298.0     73.0    298.0   \n126  2024.02.12~02.18   (630, 650]     47.0    199.0     47.0    199.0   \n127  2024.02.12~02.18  (650, 1000]     20.0    128.0     12.0    128.0   \n128  2024.02.19~02.25     (0, 530]      9.0     15.0      9.0     15.0   \n129  2024.02.19~02.25   (530, 550]     47.0     70.0     45.0     70.0   \n130  2024.02.19~02.25   (550, 570]    104.0    175.0    102.0    175.0   \n131  2024.02.19~02.25   (570, 590]    196.0    385.0    191.0    385.0   \n132  2024.02.19~02.25   (590, 610]    177.0    419.0    167.0    419.0   \n133  2024.02.19~02.25   (610, 630]     90.0    295.0     71.0    295.0   \n134  2024.02.19~02.25   (630, 650]     43.0    279.0     37.0    279.0   \n135  2024.02.19~02.25  (650, 1000]      5.0    184.0      4.0    184.0   \n136  2024.02.26~03.03     (0, 530]     11.0     20.0     11.0     20.0   \n137  2024.02.26~03.03   (530, 550]     66.0     84.0     66.0     84.0   \n138  2024.02.26~03.03   (550, 570]    126.0    227.0    117.0    227.0   \n139  2024.02.26~03.03   (570, 590]    227.0    455.0    204.0    455.0   \n140  2024.02.26~03.03   (590, 610]    183.0    512.0    173.0    512.0   \n141  2024.02.26~03.03   (610, 630]     87.0    363.0     80.0    363.0   \n142  2024.02.26~03.03   (630, 650]     45.0    249.0     35.0    249.0   \n143  2024.02.26~03.03  (650, 1000]      9.0    224.0      5.0    224.0   \n144  2024.03.04~03.10     (0, 530]     16.0     30.0     16.0     30.0   \n145  2024.03.04~03.10   (530, 550]     55.0     78.0     52.0     78.0   \n146  2024.03.04~03.10   (550, 570]    144.0    226.0    138.0    226.0   \n147  2024.03.04~03.10   (570, 590]    251.0    464.0    230.0    464.0   \n148  2024.03.04~03.10   (590, 610]    173.0    477.0    160.0    477.0   \n149  2024.03.04~03.10   (610, 630]    128.0    385.0    121.0    385.0   \n150  2024.03.04~03.10   (630, 650]     32.0    284.0     27.0    284.0   \n151  2024.03.04~03.10  (650, 1000]     16.0    219.0     13.0    219.0   \n152  2024.03.11~03.17     (0, 530]     10.0     10.0     10.0     10.0   \n153  2024.03.11~03.17   (530, 550]     65.0     93.0     64.0     93.0   \n154  2024.03.11~03.17   (550, 570]    144.0    244.0    141.0    244.0   \n155  2024.03.11~03.17   (570, 590]    311.0    572.0    302.0    572.0   \n156  2024.03.11~03.17   (590, 610]    222.0    492.0    207.0    492.0   \n157  2024.03.11~03.17   (610, 630]    136.0    401.0    125.0    401.0   \n158  2024.03.11~03.17   (630, 650]     71.0    307.0     63.0    307.0   \n159  2024.03.11~03.17  (650, 1000]     11.0    181.0      7.0    181.0   \n160  2024.03.18~03.24     (0, 530]      1.0      2.0      1.0      2.0   \n161  2024.03.18~03.24   (530, 550]     20.0     25.0     21.0     26.0   \n162  2024.03.18~03.24   (550, 570]     52.0     83.0     54.0     89.0   \n163  2024.03.18~03.24   (570, 590]    108.0    225.0    110.0    233.0   \n164  2024.03.18~03.24   (590, 610]     71.0    166.0     72.0    187.0   \n165  2024.03.18~03.24   (610, 630]     36.0    131.0     38.0    149.0   \n166  2024.03.18~03.24   (630, 650]     17.0    142.0     15.0    150.0   \n167  2024.03.18~03.24  (650, 1000]     18.0     68.0     18.0     77.0   \n168  2024.03.25~03.31     (0, 530]      0.0      0.0      0.0      0.0   \n169  2024.03.25~03.31   (530, 550]      0.0      0.0      0.0      0.0   \n170  2024.03.25~03.31   (550, 570]      0.0      0.0      0.0      0.0   \n171  2024.03.25~03.31   (570, 590]      0.0      0.0      0.0      0.0   \n172  2024.03.25~03.31   (590, 610]      0.0      0.0      0.0      0.0   \n173  2024.03.25~03.31   (610, 630]      0.0      0.0      0.0      0.0   \n174  2024.03.25~03.31   (630, 650]      0.0      0.0      0.0      0.0   \n175  2024.03.25~03.31  (650, 1000]      0.0      0.0      0.0      0.0   \n\n     app_order_id  user_id       pd1       cpd  \n0               0        0       NaN       NaN  \n1               0        0       NaN       NaN  \n2               9        3  0.555556  0.555556  \n3               8        3  0.750000  0.750000  \n4               7        2  0.142857  0.142857  \n5               3        1  0.000000  0.000000  \n6               0        0       NaN       NaN  \n7               0        0       NaN       NaN  \n8               1        1  1.000000  1.000000  \n9               9        3  0.555556  0.555556  \n10             11        5  0.636364  0.636364  \n11             24       10  0.541667  0.416667  \n12             77       26  0.311688  0.285714  \n13            100       35  0.160000  0.160000  \n14             37       12  0.135135  0.135135  \n15             45       15  0.044444  0.044444  \n16              5        2  0.800000  0.800000  \n17             13        6  0.692308  0.692308  \n18             70       26  0.571429  0.485714  \n19            145       56  0.358621  0.344828  \n20            189       70  0.407407  0.380952  \n21            237       88  0.210970  0.206751  \n22            106       40  0.207547  0.198113  \n23            107       35  0.102804  0.084112  \n24              5        1  0.000000  0.000000  \n25             41       22  0.560976  0.512195  \n26            140       58  0.528571  0.500000  \n27            321      120  0.517134  0.501558  \n28            409      154  0.342298  0.317848  \n29            278      109  0.190647  0.154676  \n30            153       58  0.104575  0.091503  \n31            116       34  0.051724  0.051724  \n32             23        8  0.739130  0.565217  \n33             71       21  0.633803  0.563380  \n34            235       73  0.523404  0.468085  \n35            588      151  0.467687  0.442177  \n36            552      152  0.389493  0.375000  \n37            393      105  0.246819  0.213740  \n38            252       65  0.107143  0.095238  \n39            125       34  0.088000  0.064000  \n40             19        7  0.473684  0.473684  \n41             74       28  0.500000  0.459459  \n42            312       79  0.663462  0.615385  \n43            591      153  0.532995  0.499154  \n44            586      147  0.457338  0.430034  \n45            488      124  0.272541  0.237705  \n46            263       67  0.220532  0.193916  \n47            140       29  0.114286  0.107143  \n48             18        5  0.444444  0.444444  \n49             58       21  0.672414  0.637931  \n50            155       51  0.580645  0.561290  \n51            343      102  0.498542  0.475219  \n52            361      108  0.329640  0.315789  \n53            299       91  0.220736  0.187291  \n54            185       53  0.340541  0.318919  \n55            115       26  0.130435  0.113043  \n56             56       16  0.892857  0.875000  \n57             82       26  0.646341  0.609756  \n58            150       46  0.740000  0.646667  \n59            327      101  0.486239  0.458716  \n60            359      105  0.311978  0.286908  \n61            276       70  0.206522  0.192029  \n62            139       43  0.366906  0.338129  \n63             65       19  0.076923  0.061538  \n64             36        9  0.944444  0.888889  \n65             62       22  0.725806  0.500000  \n66            154       52  0.675325  0.629870  \n67            399      121  0.511278  0.481203  \n68            464      132  0.338362  0.314655  \n69            324       89  0.265432  0.234568  \n70            157       47  0.133758  0.114650  \n71             82       18  0.012195  0.000000  \n72              4        2  1.000000  0.750000  \n73             54       18  0.703704  0.648148  \n74            179       64  0.603352  0.586592  \n75            451      145  0.485588  0.463415  \n76            386      127  0.264249  0.225389  \n77            370      110  0.227027  0.213514  \n78            171       52  0.187135  0.152047  \n79            123       27  0.056911  0.048780  \n80              4        1  0.500000  0.500000  \n81             54       19  0.777778  0.740741  \n82            224       79  0.566964  0.531250  \n83            393      133  0.391858  0.356234  \n84            430      146  0.381395  0.341860  \n85            319      114  0.228840  0.203762  \n86            192       65  0.135417  0.119792  \n87            107       31  0.093458  0.065421  \n88             12        3  0.666667  0.666667  \n89             92       27  0.641304  0.641304  \n90            231       82  0.575758  0.558442  \n91            483      148  0.436853  0.414079  \n92            455      151  0.393407  0.347253  \n93            337      110  0.281899  0.258160  \n94            209       63  0.177033  0.133971  \n95            108       34  0.166667  0.129630  \n96             12        2  0.916667  0.916667  \n97             81       28  0.543210  0.530864  \n98            224       80  0.526786  0.468750  \n99            435      147  0.443678  0.386207  \n100           435      157  0.360920  0.291954  \n101           299       93  0.284281  0.264214  \n102           196       67  0.275510  0.224490  \n103           101       33  0.118812  0.099010  \n104            31       10  0.483871  0.483871  \n105           102       31  0.558824  0.509804  \n106           273       89  0.520147  0.501832  \n107           555      157  0.457658  0.405405  \n108           477      148  0.379455  0.320755  \n109           341      102  0.281525  0.246334  \n110           238       74  0.247899  0.231092  \n111            93       28  0.053763  0.021505  \n112            33        8  0.969697  0.939394  \n113            61       23  0.819672  0.819672  \n114           200       60  0.545000  0.540000  \n115           314       94  0.573248  0.509554  \n116           387      114  0.506460  0.465116  \n117           283       90  0.388693  0.282686  \n118           176       50  0.102273  0.090909  \n119           107       30  0.093458  0.074766  \n120             7        3  1.000000  1.000000  \n121            52       17  0.750000  0.692308  \n122           195       54  0.723077  0.635897  \n123           308       89  0.538961  0.506494  \n124           345       94  0.565217  0.486957  \n125           298       81  0.312081  0.244966  \n126           199       42  0.236181  0.236181  \n127           128       33  0.156250  0.093750  \n128            15        6  0.600000  0.600000  \n129            70       21  0.671429  0.642857  \n130           175       58  0.594286  0.582857  \n131           385      121  0.509091  0.496104  \n132           419      124  0.422434  0.398568  \n133           295       95  0.305085  0.240678  \n134           279       82  0.154122  0.132616  \n135           184       45  0.027174  0.021739  \n136            20       10  0.550000  0.550000  \n137            84       23  0.785714  0.785714  \n138           227       72  0.555066  0.515419  \n139           455      150  0.498901  0.448352  \n140           512      153  0.357422  0.337891  \n141           363      119  0.239669  0.220386  \n142           249       71  0.180723  0.140562  \n143           224       55  0.040179  0.022321  \n144            30       12  0.533333  0.533333  \n145            78       30  0.705128  0.666667  \n146           226       92  0.637168  0.610619  \n147           464      161  0.540948  0.495690  \n148           477      165  0.362683  0.335430  \n149           385      125  0.332468  0.314286  \n150           284       77  0.112676  0.095070  \n151           219       52  0.073059  0.059361  \n152            10        4  1.000000  1.000000  \n153            93       29  0.698925  0.688172  \n154           244       82  0.590164  0.577869  \n155           572      169  0.543706  0.527972  \n156           492      165  0.451220  0.420732  \n157           401      123  0.339152  0.311721  \n158           307       83  0.231270  0.205212  \n159           181       50  0.060773  0.038674  \n160             2        2  0.500000  0.500000  \n161            34       17  0.800000  0.807692  \n162           131       52  0.626506  0.606742  \n163           303      112  0.480000  0.472103  \n164           249       95  0.427711  0.385027  \n165           225       79  0.274809  0.255034  \n166           227       68  0.119718  0.100000  \n167            93       29  0.264706  0.233766  \n168             4        1       NaN       NaN  \n169            10        4       NaN       NaN  \n170            57       25       NaN       NaN  \n171           133       60       NaN       NaN  \n172           145       66       NaN       NaN  \n173           170       63       NaN       NaN  \n174           120       49       NaN       NaN  \n175            73       24       NaN       NaN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>loan_week</th>\n      <th>score_bin</th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>app_order_id</th>\n      <th>user_id</th>\n      <th>pd1</th>\n      <th>cpd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023.10.30~11.05</td>\n      <td>(0, 530]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023.10.30~11.05</td>\n      <td>(530, 550]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023.10.30~11.05</td>\n      <td>(550, 570]</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>0.555556</td>\n      <td>0.555556</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023.10.30~11.05</td>\n      <td>(570, 590]</td>\n      <td>6.0</td>\n      <td>8.0</td>\n      <td>6.0</td>\n      <td>8.0</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0.750000</td>\n      <td>0.750000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023.10.30~11.05</td>\n      <td>(590, 610]</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>7.0</td>\n      <td>7</td>\n      <td>2</td>\n      <td>0.142857</td>\n      <td>0.142857</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023.10.30~11.05</td>\n      <td>(610, 630]</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023.10.30~11.05</td>\n      <td>(630, 650]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023.10.30~11.05</td>\n      <td>(650, 1000]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023.11.06~11.12</td>\n      <td>(0, 530]</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023.11.06~11.12</td>\n      <td>(530, 550]</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>5.0</td>\n      <td>9.0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>0.555556</td>\n      <td>0.555556</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023.11.06~11.12</td>\n      <td>(550, 570]</td>\n      <td>7.0</td>\n      <td>11.0</td>\n      <td>7.0</td>\n      <td>11.0</td>\n      <td>11</td>\n      <td>5</td>\n      <td>0.636364</td>\n      <td>0.636364</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023.11.06~11.12</td>\n      <td>(570, 590]</td>\n      <td>13.0</td>\n      <td>24.0</td>\n      <td>10.0</td>\n      <td>24.0</td>\n      <td>24</td>\n      <td>10</td>\n      <td>0.541667</td>\n      <td>0.416667</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023.11.06~11.12</td>\n      <td>(590, 610]</td>\n      <td>24.0</td>\n      <td>77.0</td>\n      <td>22.0</td>\n      <td>77.0</td>\n      <td>77</td>\n      <td>26</td>\n      <td>0.311688</td>\n      <td>0.285714</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023.11.06~11.12</td>\n      <td>(610, 630]</td>\n      <td>16.0</td>\n      <td>100.0</td>\n      <td>16.0</td>\n      <td>100.0</td>\n      <td>100</td>\n      <td>35</td>\n      <td>0.160000</td>\n      <td>0.160000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023.11.06~11.12</td>\n      <td>(630, 650]</td>\n      <td>5.0</td>\n      <td>37.0</td>\n      <td>5.0</td>\n      <td>37.0</td>\n      <td>37</td>\n      <td>12</td>\n      <td>0.135135</td>\n      <td>0.135135</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2023.11.06~11.12</td>\n      <td>(650, 1000]</td>\n      <td>2.0</td>\n      <td>45.0</td>\n      <td>2.0</td>\n      <td>45.0</td>\n      <td>45</td>\n      <td>15</td>\n      <td>0.044444</td>\n      <td>0.044444</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2023.11.13~11.19</td>\n      <td>(0, 530]</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0.800000</td>\n      <td>0.800000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2023.11.13~11.19</td>\n      <td>(530, 550]</td>\n      <td>9.0</td>\n      <td>13.0</td>\n      <td>9.0</td>\n      <td>13.0</td>\n      <td>13</td>\n      <td>6</td>\n      <td>0.692308</td>\n      <td>0.692308</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2023.11.13~11.19</td>\n      <td>(550, 570]</td>\n      <td>40.0</td>\n      <td>70.0</td>\n      <td>34.0</td>\n      <td>70.0</td>\n      <td>70</td>\n      <td>26</td>\n      <td>0.571429</td>\n      <td>0.485714</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2023.11.13~11.19</td>\n      <td>(570, 590]</td>\n      <td>52.0</td>\n      <td>145.0</td>\n      <td>50.0</td>\n      <td>145.0</td>\n      <td>145</td>\n      <td>56</td>\n      <td>0.358621</td>\n      <td>0.344828</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2023.11.13~11.19</td>\n      <td>(590, 610]</td>\n      <td>77.0</td>\n      <td>189.0</td>\n      <td>72.0</td>\n      <td>189.0</td>\n      <td>189</td>\n      <td>70</td>\n      <td>0.407407</td>\n      <td>0.380952</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2023.11.13~11.19</td>\n      <td>(610, 630]</td>\n      <td>50.0</td>\n      <td>237.0</td>\n      <td>49.0</td>\n      <td>237.0</td>\n      <td>237</td>\n      <td>88</td>\n      <td>0.210970</td>\n      <td>0.206751</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2023.11.13~11.19</td>\n      <td>(630, 650]</td>\n      <td>22.0</td>\n      <td>106.0</td>\n      <td>21.0</td>\n      <td>106.0</td>\n      <td>106</td>\n      <td>40</td>\n      <td>0.207547</td>\n      <td>0.198113</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2023.11.13~11.19</td>\n      <td>(650, 1000]</td>\n      <td>11.0</td>\n      <td>107.0</td>\n      <td>9.0</td>\n      <td>107.0</td>\n      <td>107</td>\n      <td>35</td>\n      <td>0.102804</td>\n      <td>0.084112</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2023.11.20~11.26</td>\n      <td>(0, 530]</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2023.11.20~11.26</td>\n      <td>(530, 550]</td>\n      <td>23.0</td>\n      <td>41.0</td>\n      <td>21.0</td>\n      <td>41.0</td>\n      <td>41</td>\n      <td>22</td>\n      <td>0.560976</td>\n      <td>0.512195</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2023.11.20~11.26</td>\n      <td>(550, 570]</td>\n      <td>74.0</td>\n      <td>140.0</td>\n      <td>70.0</td>\n      <td>140.0</td>\n      <td>140</td>\n      <td>58</td>\n      <td>0.528571</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2023.11.20~11.26</td>\n      <td>(570, 590]</td>\n      <td>166.0</td>\n      <td>321.0</td>\n      <td>161.0</td>\n      <td>321.0</td>\n      <td>321</td>\n      <td>120</td>\n      <td>0.517134</td>\n      <td>0.501558</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2023.11.20~11.26</td>\n      <td>(590, 610]</td>\n      <td>140.0</td>\n      <td>409.0</td>\n      <td>130.0</td>\n      <td>409.0</td>\n      <td>409</td>\n      <td>154</td>\n      <td>0.342298</td>\n      <td>0.317848</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2023.11.20~11.26</td>\n      <td>(610, 630]</td>\n      <td>53.0</td>\n      <td>278.0</td>\n      <td>43.0</td>\n      <td>278.0</td>\n      <td>278</td>\n      <td>109</td>\n      <td>0.190647</td>\n      <td>0.154676</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>2023.11.20~11.26</td>\n      <td>(630, 650]</td>\n      <td>16.0</td>\n      <td>153.0</td>\n      <td>14.0</td>\n      <td>153.0</td>\n      <td>153</td>\n      <td>58</td>\n      <td>0.104575</td>\n      <td>0.091503</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>2023.11.20~11.26</td>\n      <td>(650, 1000]</td>\n      <td>6.0</td>\n      <td>116.0</td>\n      <td>6.0</td>\n      <td>116.0</td>\n      <td>116</td>\n      <td>34</td>\n      <td>0.051724</td>\n      <td>0.051724</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>2023.11.27~12.03</td>\n      <td>(0, 530]</td>\n      <td>17.0</td>\n      <td>23.0</td>\n      <td>13.0</td>\n      <td>23.0</td>\n      <td>23</td>\n      <td>8</td>\n      <td>0.739130</td>\n      <td>0.565217</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2023.11.27~12.03</td>\n      <td>(530, 550]</td>\n      <td>45.0</td>\n      <td>71.0</td>\n      <td>40.0</td>\n      <td>71.0</td>\n      <td>71</td>\n      <td>21</td>\n      <td>0.633803</td>\n      <td>0.563380</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2023.11.27~12.03</td>\n      <td>(550, 570]</td>\n      <td>123.0</td>\n      <td>235.0</td>\n      <td>110.0</td>\n      <td>235.0</td>\n      <td>235</td>\n      <td>73</td>\n      <td>0.523404</td>\n      <td>0.468085</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2023.11.27~12.03</td>\n      <td>(570, 590]</td>\n      <td>275.0</td>\n      <td>588.0</td>\n      <td>260.0</td>\n      <td>588.0</td>\n      <td>588</td>\n      <td>151</td>\n      <td>0.467687</td>\n      <td>0.442177</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>2023.11.27~12.03</td>\n      <td>(590, 610]</td>\n      <td>215.0</td>\n      <td>552.0</td>\n      <td>207.0</td>\n      <td>552.0</td>\n      <td>552</td>\n      <td>152</td>\n      <td>0.389493</td>\n      <td>0.375000</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>2023.11.27~12.03</td>\n      <td>(610, 630]</td>\n      <td>97.0</td>\n      <td>393.0</td>\n      <td>84.0</td>\n      <td>393.0</td>\n      <td>393</td>\n      <td>105</td>\n      <td>0.246819</td>\n      <td>0.213740</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>2023.11.27~12.03</td>\n      <td>(630, 650]</td>\n      <td>27.0</td>\n      <td>252.0</td>\n      <td>24.0</td>\n      <td>252.0</td>\n      <td>252</td>\n      <td>65</td>\n      <td>0.107143</td>\n      <td>0.095238</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>2023.11.27~12.03</td>\n      <td>(650, 1000]</td>\n      <td>11.0</td>\n      <td>125.0</td>\n      <td>8.0</td>\n      <td>125.0</td>\n      <td>125</td>\n      <td>34</td>\n      <td>0.088000</td>\n      <td>0.064000</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>2023.12.04~12.10</td>\n      <td>(0, 530]</td>\n      <td>9.0</td>\n      <td>19.0</td>\n      <td>9.0</td>\n      <td>19.0</td>\n      <td>19</td>\n      <td>7</td>\n      <td>0.473684</td>\n      <td>0.473684</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>2023.12.04~12.10</td>\n      <td>(530, 550]</td>\n      <td>37.0</td>\n      <td>74.0</td>\n      <td>34.0</td>\n      <td>74.0</td>\n      <td>74</td>\n      <td>28</td>\n      <td>0.500000</td>\n      <td>0.459459</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>2023.12.04~12.10</td>\n      <td>(550, 570]</td>\n      <td>207.0</td>\n      <td>312.0</td>\n      <td>192.0</td>\n      <td>312.0</td>\n      <td>312</td>\n      <td>79</td>\n      <td>0.663462</td>\n      <td>0.615385</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>2023.12.04~12.10</td>\n      <td>(570, 590]</td>\n      <td>315.0</td>\n      <td>591.0</td>\n      <td>295.0</td>\n      <td>591.0</td>\n      <td>591</td>\n      <td>153</td>\n      <td>0.532995</td>\n      <td>0.499154</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>2023.12.04~12.10</td>\n      <td>(590, 610]</td>\n      <td>268.0</td>\n      <td>586.0</td>\n      <td>252.0</td>\n      <td>586.0</td>\n      <td>586</td>\n      <td>147</td>\n      <td>0.457338</td>\n      <td>0.430034</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>2023.12.04~12.10</td>\n      <td>(610, 630]</td>\n      <td>133.0</td>\n      <td>488.0</td>\n      <td>116.0</td>\n      <td>488.0</td>\n      <td>488</td>\n      <td>124</td>\n      <td>0.272541</td>\n      <td>0.237705</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>2023.12.04~12.10</td>\n      <td>(630, 650]</td>\n      <td>58.0</td>\n      <td>263.0</td>\n      <td>51.0</td>\n      <td>263.0</td>\n      <td>263</td>\n      <td>67</td>\n      <td>0.220532</td>\n      <td>0.193916</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>2023.12.04~12.10</td>\n      <td>(650, 1000]</td>\n      <td>16.0</td>\n      <td>140.0</td>\n      <td>15.0</td>\n      <td>140.0</td>\n      <td>140</td>\n      <td>29</td>\n      <td>0.114286</td>\n      <td>0.107143</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>2023.12.11~12.17</td>\n      <td>(0, 530]</td>\n      <td>8.0</td>\n      <td>18.0</td>\n      <td>8.0</td>\n      <td>18.0</td>\n      <td>18</td>\n      <td>5</td>\n      <td>0.444444</td>\n      <td>0.444444</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>2023.12.11~12.17</td>\n      <td>(530, 550]</td>\n      <td>39.0</td>\n      <td>58.0</td>\n      <td>37.0</td>\n      <td>58.0</td>\n      <td>58</td>\n      <td>21</td>\n      <td>0.672414</td>\n      <td>0.637931</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>2023.12.11~12.17</td>\n      <td>(550, 570]</td>\n      <td>90.0</td>\n      <td>155.0</td>\n      <td>87.0</td>\n      <td>155.0</td>\n      <td>155</td>\n      <td>51</td>\n      <td>0.580645</td>\n      <td>0.561290</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>2023.12.11~12.17</td>\n      <td>(570, 590]</td>\n      <td>171.0</td>\n      <td>343.0</td>\n      <td>163.0</td>\n      <td>343.0</td>\n      <td>343</td>\n      <td>102</td>\n      <td>0.498542</td>\n      <td>0.475219</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>2023.12.11~12.17</td>\n      <td>(590, 610]</td>\n      <td>119.0</td>\n      <td>361.0</td>\n      <td>114.0</td>\n      <td>361.0</td>\n      <td>361</td>\n      <td>108</td>\n      <td>0.329640</td>\n      <td>0.315789</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2023.12.11~12.17</td>\n      <td>(610, 630]</td>\n      <td>66.0</td>\n      <td>299.0</td>\n      <td>56.0</td>\n      <td>299.0</td>\n      <td>299</td>\n      <td>91</td>\n      <td>0.220736</td>\n      <td>0.187291</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>2023.12.11~12.17</td>\n      <td>(630, 650]</td>\n      <td>63.0</td>\n      <td>185.0</td>\n      <td>59.0</td>\n      <td>185.0</td>\n      <td>185</td>\n      <td>53</td>\n      <td>0.340541</td>\n      <td>0.318919</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>2023.12.11~12.17</td>\n      <td>(650, 1000]</td>\n      <td>15.0</td>\n      <td>115.0</td>\n      <td>13.0</td>\n      <td>115.0</td>\n      <td>115</td>\n      <td>26</td>\n      <td>0.130435</td>\n      <td>0.113043</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>2023.12.18~12.24</td>\n      <td>(0, 530]</td>\n      <td>50.0</td>\n      <td>56.0</td>\n      <td>49.0</td>\n      <td>56.0</td>\n      <td>56</td>\n      <td>16</td>\n      <td>0.892857</td>\n      <td>0.875000</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>2023.12.18~12.24</td>\n      <td>(530, 550]</td>\n      <td>53.0</td>\n      <td>82.0</td>\n      <td>50.0</td>\n      <td>82.0</td>\n      <td>82</td>\n      <td>26</td>\n      <td>0.646341</td>\n      <td>0.609756</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>2023.12.18~12.24</td>\n      <td>(550, 570]</td>\n      <td>111.0</td>\n      <td>150.0</td>\n      <td>97.0</td>\n      <td>150.0</td>\n      <td>150</td>\n      <td>46</td>\n      <td>0.740000</td>\n      <td>0.646667</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>2023.12.18~12.24</td>\n      <td>(570, 590]</td>\n      <td>159.0</td>\n      <td>327.0</td>\n      <td>150.0</td>\n      <td>327.0</td>\n      <td>327</td>\n      <td>101</td>\n      <td>0.486239</td>\n      <td>0.458716</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>2023.12.18~12.24</td>\n      <td>(590, 610]</td>\n      <td>112.0</td>\n      <td>359.0</td>\n      <td>103.0</td>\n      <td>359.0</td>\n      <td>359</td>\n      <td>105</td>\n      <td>0.311978</td>\n      <td>0.286908</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>2023.12.18~12.24</td>\n      <td>(610, 630]</td>\n      <td>57.0</td>\n      <td>276.0</td>\n      <td>53.0</td>\n      <td>276.0</td>\n      <td>276</td>\n      <td>70</td>\n      <td>0.206522</td>\n      <td>0.192029</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>2023.12.18~12.24</td>\n      <td>(630, 650]</td>\n      <td>51.0</td>\n      <td>139.0</td>\n      <td>47.0</td>\n      <td>139.0</td>\n      <td>139</td>\n      <td>43</td>\n      <td>0.366906</td>\n      <td>0.338129</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>2023.12.18~12.24</td>\n      <td>(650, 1000]</td>\n      <td>5.0</td>\n      <td>65.0</td>\n      <td>4.0</td>\n      <td>65.0</td>\n      <td>65</td>\n      <td>19</td>\n      <td>0.076923</td>\n      <td>0.061538</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>2023.12.25~12.31</td>\n      <td>(0, 530]</td>\n      <td>34.0</td>\n      <td>36.0</td>\n      <td>32.0</td>\n      <td>36.0</td>\n      <td>36</td>\n      <td>9</td>\n      <td>0.944444</td>\n      <td>0.888889</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>2023.12.25~12.31</td>\n      <td>(530, 550]</td>\n      <td>45.0</td>\n      <td>62.0</td>\n      <td>31.0</td>\n      <td>62.0</td>\n      <td>62</td>\n      <td>22</td>\n      <td>0.725806</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>2023.12.25~12.31</td>\n      <td>(550, 570]</td>\n      <td>104.0</td>\n      <td>154.0</td>\n      <td>97.0</td>\n      <td>154.0</td>\n      <td>154</td>\n      <td>52</td>\n      <td>0.675325</td>\n      <td>0.629870</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>2023.12.25~12.31</td>\n      <td>(570, 590]</td>\n      <td>204.0</td>\n      <td>399.0</td>\n      <td>192.0</td>\n      <td>399.0</td>\n      <td>399</td>\n      <td>121</td>\n      <td>0.511278</td>\n      <td>0.481203</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>2023.12.25~12.31</td>\n      <td>(590, 610]</td>\n      <td>157.0</td>\n      <td>464.0</td>\n      <td>146.0</td>\n      <td>464.0</td>\n      <td>464</td>\n      <td>132</td>\n      <td>0.338362</td>\n      <td>0.314655</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>2023.12.25~12.31</td>\n      <td>(610, 630]</td>\n      <td>86.0</td>\n      <td>324.0</td>\n      <td>76.0</td>\n      <td>324.0</td>\n      <td>324</td>\n      <td>89</td>\n      <td>0.265432</td>\n      <td>0.234568</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>2023.12.25~12.31</td>\n      <td>(630, 650]</td>\n      <td>21.0</td>\n      <td>157.0</td>\n      <td>18.0</td>\n      <td>157.0</td>\n      <td>157</td>\n      <td>47</td>\n      <td>0.133758</td>\n      <td>0.114650</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>2023.12.25~12.31</td>\n      <td>(650, 1000]</td>\n      <td>1.0</td>\n      <td>82.0</td>\n      <td>0.0</td>\n      <td>82.0</td>\n      <td>82</td>\n      <td>18</td>\n      <td>0.012195</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2024.01.01~01.07</td>\n      <td>(0, 530]</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>4</td>\n      <td>2</td>\n      <td>1.000000</td>\n      <td>0.750000</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2024.01.01~01.07</td>\n      <td>(530, 550]</td>\n      <td>38.0</td>\n      <td>54.0</td>\n      <td>35.0</td>\n      <td>54.0</td>\n      <td>54</td>\n      <td>18</td>\n      <td>0.703704</td>\n      <td>0.648148</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2024.01.01~01.07</td>\n      <td>(550, 570]</td>\n      <td>108.0</td>\n      <td>179.0</td>\n      <td>105.0</td>\n      <td>179.0</td>\n      <td>179</td>\n      <td>64</td>\n      <td>0.603352</td>\n      <td>0.586592</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>2024.01.01~01.07</td>\n      <td>(570, 590]</td>\n      <td>219.0</td>\n      <td>451.0</td>\n      <td>209.0</td>\n      <td>451.0</td>\n      <td>451</td>\n      <td>145</td>\n      <td>0.485588</td>\n      <td>0.463415</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>2024.01.01~01.07</td>\n      <td>(590, 610]</td>\n      <td>102.0</td>\n      <td>386.0</td>\n      <td>87.0</td>\n      <td>386.0</td>\n      <td>386</td>\n      <td>127</td>\n      <td>0.264249</td>\n      <td>0.225389</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>2024.01.01~01.07</td>\n      <td>(610, 630]</td>\n      <td>84.0</td>\n      <td>370.0</td>\n      <td>79.0</td>\n      <td>370.0</td>\n      <td>370</td>\n      <td>110</td>\n      <td>0.227027</td>\n      <td>0.213514</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>2024.01.01~01.07</td>\n      <td>(630, 650]</td>\n      <td>32.0</td>\n      <td>171.0</td>\n      <td>26.0</td>\n      <td>171.0</td>\n      <td>171</td>\n      <td>52</td>\n      <td>0.187135</td>\n      <td>0.152047</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2024.01.01~01.07</td>\n      <td>(650, 1000]</td>\n      <td>7.0</td>\n      <td>123.0</td>\n      <td>6.0</td>\n      <td>123.0</td>\n      <td>123</td>\n      <td>27</td>\n      <td>0.056911</td>\n      <td>0.048780</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>2024.01.08~01.14</td>\n      <td>(0, 530]</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>2024.01.08~01.14</td>\n      <td>(530, 550]</td>\n      <td>42.0</td>\n      <td>54.0</td>\n      <td>40.0</td>\n      <td>54.0</td>\n      <td>54</td>\n      <td>19</td>\n      <td>0.777778</td>\n      <td>0.740741</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>2024.01.08~01.14</td>\n      <td>(550, 570]</td>\n      <td>127.0</td>\n      <td>224.0</td>\n      <td>119.0</td>\n      <td>224.0</td>\n      <td>224</td>\n      <td>79</td>\n      <td>0.566964</td>\n      <td>0.531250</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>2024.01.08~01.14</td>\n      <td>(570, 590]</td>\n      <td>154.0</td>\n      <td>393.0</td>\n      <td>140.0</td>\n      <td>393.0</td>\n      <td>393</td>\n      <td>133</td>\n      <td>0.391858</td>\n      <td>0.356234</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>2024.01.08~01.14</td>\n      <td>(590, 610]</td>\n      <td>164.0</td>\n      <td>430.0</td>\n      <td>147.0</td>\n      <td>430.0</td>\n      <td>430</td>\n      <td>146</td>\n      <td>0.381395</td>\n      <td>0.341860</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>2024.01.08~01.14</td>\n      <td>(610, 630]</td>\n      <td>73.0</td>\n      <td>319.0</td>\n      <td>65.0</td>\n      <td>319.0</td>\n      <td>319</td>\n      <td>114</td>\n      <td>0.228840</td>\n      <td>0.203762</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>2024.01.08~01.14</td>\n      <td>(630, 650]</td>\n      <td>26.0</td>\n      <td>192.0</td>\n      <td>23.0</td>\n      <td>192.0</td>\n      <td>192</td>\n      <td>65</td>\n      <td>0.135417</td>\n      <td>0.119792</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>2024.01.08~01.14</td>\n      <td>(650, 1000]</td>\n      <td>10.0</td>\n      <td>107.0</td>\n      <td>7.0</td>\n      <td>107.0</td>\n      <td>107</td>\n      <td>31</td>\n      <td>0.093458</td>\n      <td>0.065421</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>2024.01.15~01.21</td>\n      <td>(0, 530]</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>12</td>\n      <td>3</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>2024.01.15~01.21</td>\n      <td>(530, 550]</td>\n      <td>59.0</td>\n      <td>92.0</td>\n      <td>59.0</td>\n      <td>92.0</td>\n      <td>92</td>\n      <td>27</td>\n      <td>0.641304</td>\n      <td>0.641304</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>2024.01.15~01.21</td>\n      <td>(550, 570]</td>\n      <td>133.0</td>\n      <td>231.0</td>\n      <td>129.0</td>\n      <td>231.0</td>\n      <td>231</td>\n      <td>82</td>\n      <td>0.575758</td>\n      <td>0.558442</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>2024.01.15~01.21</td>\n      <td>(570, 590]</td>\n      <td>211.0</td>\n      <td>483.0</td>\n      <td>200.0</td>\n      <td>483.0</td>\n      <td>483</td>\n      <td>148</td>\n      <td>0.436853</td>\n      <td>0.414079</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>2024.01.15~01.21</td>\n      <td>(590, 610]</td>\n      <td>179.0</td>\n      <td>455.0</td>\n      <td>158.0</td>\n      <td>455.0</td>\n      <td>455</td>\n      <td>151</td>\n      <td>0.393407</td>\n      <td>0.347253</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>2024.01.15~01.21</td>\n      <td>(610, 630]</td>\n      <td>95.0</td>\n      <td>337.0</td>\n      <td>87.0</td>\n      <td>337.0</td>\n      <td>337</td>\n      <td>110</td>\n      <td>0.281899</td>\n      <td>0.258160</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>2024.01.15~01.21</td>\n      <td>(630, 650]</td>\n      <td>37.0</td>\n      <td>209.0</td>\n      <td>28.0</td>\n      <td>209.0</td>\n      <td>209</td>\n      <td>63</td>\n      <td>0.177033</td>\n      <td>0.133971</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>2024.01.15~01.21</td>\n      <td>(650, 1000]</td>\n      <td>18.0</td>\n      <td>108.0</td>\n      <td>14.0</td>\n      <td>108.0</td>\n      <td>108</td>\n      <td>34</td>\n      <td>0.166667</td>\n      <td>0.129630</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>2024.01.22~01.28</td>\n      <td>(0, 530]</td>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>12</td>\n      <td>2</td>\n      <td>0.916667</td>\n      <td>0.916667</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>2024.01.22~01.28</td>\n      <td>(530, 550]</td>\n      <td>44.0</td>\n      <td>81.0</td>\n      <td>43.0</td>\n      <td>81.0</td>\n      <td>81</td>\n      <td>28</td>\n      <td>0.543210</td>\n      <td>0.530864</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>2024.01.22~01.28</td>\n      <td>(550, 570]</td>\n      <td>118.0</td>\n      <td>224.0</td>\n      <td>105.0</td>\n      <td>224.0</td>\n      <td>224</td>\n      <td>80</td>\n      <td>0.526786</td>\n      <td>0.468750</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>2024.01.22~01.28</td>\n      <td>(570, 590]</td>\n      <td>193.0</td>\n      <td>435.0</td>\n      <td>168.0</td>\n      <td>435.0</td>\n      <td>435</td>\n      <td>147</td>\n      <td>0.443678</td>\n      <td>0.386207</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>2024.01.22~01.28</td>\n      <td>(590, 610]</td>\n      <td>157.0</td>\n      <td>435.0</td>\n      <td>127.0</td>\n      <td>435.0</td>\n      <td>435</td>\n      <td>157</td>\n      <td>0.360920</td>\n      <td>0.291954</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>2024.01.22~01.28</td>\n      <td>(610, 630]</td>\n      <td>85.0</td>\n      <td>299.0</td>\n      <td>79.0</td>\n      <td>299.0</td>\n      <td>299</td>\n      <td>93</td>\n      <td>0.284281</td>\n      <td>0.264214</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>2024.01.22~01.28</td>\n      <td>(630, 650]</td>\n      <td>54.0</td>\n      <td>196.0</td>\n      <td>44.0</td>\n      <td>196.0</td>\n      <td>196</td>\n      <td>67</td>\n      <td>0.275510</td>\n      <td>0.224490</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>2024.01.22~01.28</td>\n      <td>(650, 1000]</td>\n      <td>12.0</td>\n      <td>101.0</td>\n      <td>10.0</td>\n      <td>101.0</td>\n      <td>101</td>\n      <td>33</td>\n      <td>0.118812</td>\n      <td>0.099010</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>2024.01.29~02.04</td>\n      <td>(0, 530]</td>\n      <td>15.0</td>\n      <td>31.0</td>\n      <td>15.0</td>\n      <td>31.0</td>\n      <td>31</td>\n      <td>10</td>\n      <td>0.483871</td>\n      <td>0.483871</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>2024.01.29~02.04</td>\n      <td>(530, 550]</td>\n      <td>57.0</td>\n      <td>102.0</td>\n      <td>52.0</td>\n      <td>102.0</td>\n      <td>102</td>\n      <td>31</td>\n      <td>0.558824</td>\n      <td>0.509804</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>2024.01.29~02.04</td>\n      <td>(550, 570]</td>\n      <td>142.0</td>\n      <td>273.0</td>\n      <td>137.0</td>\n      <td>273.0</td>\n      <td>273</td>\n      <td>89</td>\n      <td>0.520147</td>\n      <td>0.501832</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>2024.01.29~02.04</td>\n      <td>(570, 590]</td>\n      <td>254.0</td>\n      <td>555.0</td>\n      <td>225.0</td>\n      <td>555.0</td>\n      <td>555</td>\n      <td>157</td>\n      <td>0.457658</td>\n      <td>0.405405</td>\n    </tr>\n    <tr>\n      <th>108</th>\n      <td>2024.01.29~02.04</td>\n      <td>(590, 610]</td>\n      <td>181.0</td>\n      <td>477.0</td>\n      <td>153.0</td>\n      <td>477.0</td>\n      <td>477</td>\n      <td>148</td>\n      <td>0.379455</td>\n      <td>0.320755</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>2024.01.29~02.04</td>\n      <td>(610, 630]</td>\n      <td>96.0</td>\n      <td>341.0</td>\n      <td>84.0</td>\n      <td>341.0</td>\n      <td>341</td>\n      <td>102</td>\n      <td>0.281525</td>\n      <td>0.246334</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>2024.01.29~02.04</td>\n      <td>(630, 650]</td>\n      <td>59.0</td>\n      <td>238.0</td>\n      <td>55.0</td>\n      <td>238.0</td>\n      <td>238</td>\n      <td>74</td>\n      <td>0.247899</td>\n      <td>0.231092</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>2024.01.29~02.04</td>\n      <td>(650, 1000]</td>\n      <td>5.0</td>\n      <td>93.0</td>\n      <td>2.0</td>\n      <td>93.0</td>\n      <td>93</td>\n      <td>28</td>\n      <td>0.053763</td>\n      <td>0.021505</td>\n    </tr>\n    <tr>\n      <th>112</th>\n      <td>2024.02.05~02.11</td>\n      <td>(0, 530]</td>\n      <td>32.0</td>\n      <td>33.0</td>\n      <td>31.0</td>\n      <td>33.0</td>\n      <td>33</td>\n      <td>8</td>\n      <td>0.969697</td>\n      <td>0.939394</td>\n    </tr>\n    <tr>\n      <th>113</th>\n      <td>2024.02.05~02.11</td>\n      <td>(530, 550]</td>\n      <td>50.0</td>\n      <td>61.0</td>\n      <td>50.0</td>\n      <td>61.0</td>\n      <td>61</td>\n      <td>23</td>\n      <td>0.819672</td>\n      <td>0.819672</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>2024.02.05~02.11</td>\n      <td>(550, 570]</td>\n      <td>109.0</td>\n      <td>200.0</td>\n      <td>108.0</td>\n      <td>200.0</td>\n      <td>200</td>\n      <td>60</td>\n      <td>0.545000</td>\n      <td>0.540000</td>\n    </tr>\n    <tr>\n      <th>115</th>\n      <td>2024.02.05~02.11</td>\n      <td>(570, 590]</td>\n      <td>180.0</td>\n      <td>314.0</td>\n      <td>160.0</td>\n      <td>314.0</td>\n      <td>314</td>\n      <td>94</td>\n      <td>0.573248</td>\n      <td>0.509554</td>\n    </tr>\n    <tr>\n      <th>116</th>\n      <td>2024.02.05~02.11</td>\n      <td>(590, 610]</td>\n      <td>196.0</td>\n      <td>387.0</td>\n      <td>180.0</td>\n      <td>387.0</td>\n      <td>387</td>\n      <td>114</td>\n      <td>0.506460</td>\n      <td>0.465116</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>2024.02.05~02.11</td>\n      <td>(610, 630]</td>\n      <td>110.0</td>\n      <td>283.0</td>\n      <td>80.0</td>\n      <td>283.0</td>\n      <td>283</td>\n      <td>90</td>\n      <td>0.388693</td>\n      <td>0.282686</td>\n    </tr>\n    <tr>\n      <th>118</th>\n      <td>2024.02.05~02.11</td>\n      <td>(630, 650]</td>\n      <td>18.0</td>\n      <td>176.0</td>\n      <td>16.0</td>\n      <td>176.0</td>\n      <td>176</td>\n      <td>50</td>\n      <td>0.102273</td>\n      <td>0.090909</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>2024.02.05~02.11</td>\n      <td>(650, 1000]</td>\n      <td>10.0</td>\n      <td>107.0</td>\n      <td>8.0</td>\n      <td>107.0</td>\n      <td>107</td>\n      <td>30</td>\n      <td>0.093458</td>\n      <td>0.074766</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>2024.02.12~02.18</td>\n      <td>(0, 530]</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7</td>\n      <td>3</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>121</th>\n      <td>2024.02.12~02.18</td>\n      <td>(530, 550]</td>\n      <td>39.0</td>\n      <td>52.0</td>\n      <td>36.0</td>\n      <td>52.0</td>\n      <td>52</td>\n      <td>17</td>\n      <td>0.750000</td>\n      <td>0.692308</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>2024.02.12~02.18</td>\n      <td>(550, 570]</td>\n      <td>141.0</td>\n      <td>195.0</td>\n      <td>124.0</td>\n      <td>195.0</td>\n      <td>195</td>\n      <td>54</td>\n      <td>0.723077</td>\n      <td>0.635897</td>\n    </tr>\n    <tr>\n      <th>123</th>\n      <td>2024.02.12~02.18</td>\n      <td>(570, 590]</td>\n      <td>166.0</td>\n      <td>308.0</td>\n      <td>156.0</td>\n      <td>308.0</td>\n      <td>308</td>\n      <td>89</td>\n      <td>0.538961</td>\n      <td>0.506494</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>2024.02.12~02.18</td>\n      <td>(590, 610]</td>\n      <td>195.0</td>\n      <td>345.0</td>\n      <td>168.0</td>\n      <td>345.0</td>\n      <td>345</td>\n      <td>94</td>\n      <td>0.565217</td>\n      <td>0.486957</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>2024.02.12~02.18</td>\n      <td>(610, 630]</td>\n      <td>93.0</td>\n      <td>298.0</td>\n      <td>73.0</td>\n      <td>298.0</td>\n      <td>298</td>\n      <td>81</td>\n      <td>0.312081</td>\n      <td>0.244966</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>2024.02.12~02.18</td>\n      <td>(630, 650]</td>\n      <td>47.0</td>\n      <td>199.0</td>\n      <td>47.0</td>\n      <td>199.0</td>\n      <td>199</td>\n      <td>42</td>\n      <td>0.236181</td>\n      <td>0.236181</td>\n    </tr>\n    <tr>\n      <th>127</th>\n      <td>2024.02.12~02.18</td>\n      <td>(650, 1000]</td>\n      <td>20.0</td>\n      <td>128.0</td>\n      <td>12.0</td>\n      <td>128.0</td>\n      <td>128</td>\n      <td>33</td>\n      <td>0.156250</td>\n      <td>0.093750</td>\n    </tr>\n    <tr>\n      <th>128</th>\n      <td>2024.02.19~02.25</td>\n      <td>(0, 530]</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>15</td>\n      <td>6</td>\n      <td>0.600000</td>\n      <td>0.600000</td>\n    </tr>\n    <tr>\n      <th>129</th>\n      <td>2024.02.19~02.25</td>\n      <td>(530, 550]</td>\n      <td>47.0</td>\n      <td>70.0</td>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>70</td>\n      <td>21</td>\n      <td>0.671429</td>\n      <td>0.642857</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>2024.02.19~02.25</td>\n      <td>(550, 570]</td>\n      <td>104.0</td>\n      <td>175.0</td>\n      <td>102.0</td>\n      <td>175.0</td>\n      <td>175</td>\n      <td>58</td>\n      <td>0.594286</td>\n      <td>0.582857</td>\n    </tr>\n    <tr>\n      <th>131</th>\n      <td>2024.02.19~02.25</td>\n      <td>(570, 590]</td>\n      <td>196.0</td>\n      <td>385.0</td>\n      <td>191.0</td>\n      <td>385.0</td>\n      <td>385</td>\n      <td>121</td>\n      <td>0.509091</td>\n      <td>0.496104</td>\n    </tr>\n    <tr>\n      <th>132</th>\n      <td>2024.02.19~02.25</td>\n      <td>(590, 610]</td>\n      <td>177.0</td>\n      <td>419.0</td>\n      <td>167.0</td>\n      <td>419.0</td>\n      <td>419</td>\n      <td>124</td>\n      <td>0.422434</td>\n      <td>0.398568</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>2024.02.19~02.25</td>\n      <td>(610, 630]</td>\n      <td>90.0</td>\n      <td>295.0</td>\n      <td>71.0</td>\n      <td>295.0</td>\n      <td>295</td>\n      <td>95</td>\n      <td>0.305085</td>\n      <td>0.240678</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>2024.02.19~02.25</td>\n      <td>(630, 650]</td>\n      <td>43.0</td>\n      <td>279.0</td>\n      <td>37.0</td>\n      <td>279.0</td>\n      <td>279</td>\n      <td>82</td>\n      <td>0.154122</td>\n      <td>0.132616</td>\n    </tr>\n    <tr>\n      <th>135</th>\n      <td>2024.02.19~02.25</td>\n      <td>(650, 1000]</td>\n      <td>5.0</td>\n      <td>184.0</td>\n      <td>4.0</td>\n      <td>184.0</td>\n      <td>184</td>\n      <td>45</td>\n      <td>0.027174</td>\n      <td>0.021739</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>2024.02.26~03.03</td>\n      <td>(0, 530]</td>\n      <td>11.0</td>\n      <td>20.0</td>\n      <td>11.0</td>\n      <td>20.0</td>\n      <td>20</td>\n      <td>10</td>\n      <td>0.550000</td>\n      <td>0.550000</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>2024.02.26~03.03</td>\n      <td>(530, 550]</td>\n      <td>66.0</td>\n      <td>84.0</td>\n      <td>66.0</td>\n      <td>84.0</td>\n      <td>84</td>\n      <td>23</td>\n      <td>0.785714</td>\n      <td>0.785714</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>2024.02.26~03.03</td>\n      <td>(550, 570]</td>\n      <td>126.0</td>\n      <td>227.0</td>\n      <td>117.0</td>\n      <td>227.0</td>\n      <td>227</td>\n      <td>72</td>\n      <td>0.555066</td>\n      <td>0.515419</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>2024.02.26~03.03</td>\n      <td>(570, 590]</td>\n      <td>227.0</td>\n      <td>455.0</td>\n      <td>204.0</td>\n      <td>455.0</td>\n      <td>455</td>\n      <td>150</td>\n      <td>0.498901</td>\n      <td>0.448352</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>2024.02.26~03.03</td>\n      <td>(590, 610]</td>\n      <td>183.0</td>\n      <td>512.0</td>\n      <td>173.0</td>\n      <td>512.0</td>\n      <td>512</td>\n      <td>153</td>\n      <td>0.357422</td>\n      <td>0.337891</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>2024.02.26~03.03</td>\n      <td>(610, 630]</td>\n      <td>87.0</td>\n      <td>363.0</td>\n      <td>80.0</td>\n      <td>363.0</td>\n      <td>363</td>\n      <td>119</td>\n      <td>0.239669</td>\n      <td>0.220386</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>2024.02.26~03.03</td>\n      <td>(630, 650]</td>\n      <td>45.0</td>\n      <td>249.0</td>\n      <td>35.0</td>\n      <td>249.0</td>\n      <td>249</td>\n      <td>71</td>\n      <td>0.180723</td>\n      <td>0.140562</td>\n    </tr>\n    <tr>\n      <th>143</th>\n      <td>2024.02.26~03.03</td>\n      <td>(650, 1000]</td>\n      <td>9.0</td>\n      <td>224.0</td>\n      <td>5.0</td>\n      <td>224.0</td>\n      <td>224</td>\n      <td>55</td>\n      <td>0.040179</td>\n      <td>0.022321</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>2024.03.04~03.10</td>\n      <td>(0, 530]</td>\n      <td>16.0</td>\n      <td>30.0</td>\n      <td>16.0</td>\n      <td>30.0</td>\n      <td>30</td>\n      <td>12</td>\n      <td>0.533333</td>\n      <td>0.533333</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>2024.03.04~03.10</td>\n      <td>(530, 550]</td>\n      <td>55.0</td>\n      <td>78.0</td>\n      <td>52.0</td>\n      <td>78.0</td>\n      <td>78</td>\n      <td>30</td>\n      <td>0.705128</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>2024.03.04~03.10</td>\n      <td>(550, 570]</td>\n      <td>144.0</td>\n      <td>226.0</td>\n      <td>138.0</td>\n      <td>226.0</td>\n      <td>226</td>\n      <td>92</td>\n      <td>0.637168</td>\n      <td>0.610619</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>2024.03.04~03.10</td>\n      <td>(570, 590]</td>\n      <td>251.0</td>\n      <td>464.0</td>\n      <td>230.0</td>\n      <td>464.0</td>\n      <td>464</td>\n      <td>161</td>\n      <td>0.540948</td>\n      <td>0.495690</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>2024.03.04~03.10</td>\n      <td>(590, 610]</td>\n      <td>173.0</td>\n      <td>477.0</td>\n      <td>160.0</td>\n      <td>477.0</td>\n      <td>477</td>\n      <td>165</td>\n      <td>0.362683</td>\n      <td>0.335430</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>2024.03.04~03.10</td>\n      <td>(610, 630]</td>\n      <td>128.0</td>\n      <td>385.0</td>\n      <td>121.0</td>\n      <td>385.0</td>\n      <td>385</td>\n      <td>125</td>\n      <td>0.332468</td>\n      <td>0.314286</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>2024.03.04~03.10</td>\n      <td>(630, 650]</td>\n      <td>32.0</td>\n      <td>284.0</td>\n      <td>27.0</td>\n      <td>284.0</td>\n      <td>284</td>\n      <td>77</td>\n      <td>0.112676</td>\n      <td>0.095070</td>\n    </tr>\n    <tr>\n      <th>151</th>\n      <td>2024.03.04~03.10</td>\n      <td>(650, 1000]</td>\n      <td>16.0</td>\n      <td>219.0</td>\n      <td>13.0</td>\n      <td>219.0</td>\n      <td>219</td>\n      <td>52</td>\n      <td>0.073059</td>\n      <td>0.059361</td>\n    </tr>\n    <tr>\n      <th>152</th>\n      <td>2024.03.11~03.17</td>\n      <td>(0, 530]</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>153</th>\n      <td>2024.03.11~03.17</td>\n      <td>(530, 550]</td>\n      <td>65.0</td>\n      <td>93.0</td>\n      <td>64.0</td>\n      <td>93.0</td>\n      <td>93</td>\n      <td>29</td>\n      <td>0.698925</td>\n      <td>0.688172</td>\n    </tr>\n    <tr>\n      <th>154</th>\n      <td>2024.03.11~03.17</td>\n      <td>(550, 570]</td>\n      <td>144.0</td>\n      <td>244.0</td>\n      <td>141.0</td>\n      <td>244.0</td>\n      <td>244</td>\n      <td>82</td>\n      <td>0.590164</td>\n      <td>0.577869</td>\n    </tr>\n    <tr>\n      <th>155</th>\n      <td>2024.03.11~03.17</td>\n      <td>(570, 590]</td>\n      <td>311.0</td>\n      <td>572.0</td>\n      <td>302.0</td>\n      <td>572.0</td>\n      <td>572</td>\n      <td>169</td>\n      <td>0.543706</td>\n      <td>0.527972</td>\n    </tr>\n    <tr>\n      <th>156</th>\n      <td>2024.03.11~03.17</td>\n      <td>(590, 610]</td>\n      <td>222.0</td>\n      <td>492.0</td>\n      <td>207.0</td>\n      <td>492.0</td>\n      <td>492</td>\n      <td>165</td>\n      <td>0.451220</td>\n      <td>0.420732</td>\n    </tr>\n    <tr>\n      <th>157</th>\n      <td>2024.03.11~03.17</td>\n      <td>(610, 630]</td>\n      <td>136.0</td>\n      <td>401.0</td>\n      <td>125.0</td>\n      <td>401.0</td>\n      <td>401</td>\n      <td>123</td>\n      <td>0.339152</td>\n      <td>0.311721</td>\n    </tr>\n    <tr>\n      <th>158</th>\n      <td>2024.03.11~03.17</td>\n      <td>(630, 650]</td>\n      <td>71.0</td>\n      <td>307.0</td>\n      <td>63.0</td>\n      <td>307.0</td>\n      <td>307</td>\n      <td>83</td>\n      <td>0.231270</td>\n      <td>0.205212</td>\n    </tr>\n    <tr>\n      <th>159</th>\n      <td>2024.03.11~03.17</td>\n      <td>(650, 1000]</td>\n      <td>11.0</td>\n      <td>181.0</td>\n      <td>7.0</td>\n      <td>181.0</td>\n      <td>181</td>\n      <td>50</td>\n      <td>0.060773</td>\n      <td>0.038674</td>\n    </tr>\n    <tr>\n      <th>160</th>\n      <td>2024.03.18~03.24</td>\n      <td>(0, 530]</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>161</th>\n      <td>2024.03.18~03.24</td>\n      <td>(530, 550]</td>\n      <td>20.0</td>\n      <td>25.0</td>\n      <td>21.0</td>\n      <td>26.0</td>\n      <td>34</td>\n      <td>17</td>\n      <td>0.800000</td>\n      <td>0.807692</td>\n    </tr>\n    <tr>\n      <th>162</th>\n      <td>2024.03.18~03.24</td>\n      <td>(550, 570]</td>\n      <td>52.0</td>\n      <td>83.0</td>\n      <td>54.0</td>\n      <td>89.0</td>\n      <td>131</td>\n      <td>52</td>\n      <td>0.626506</td>\n      <td>0.606742</td>\n    </tr>\n    <tr>\n      <th>163</th>\n      <td>2024.03.18~03.24</td>\n      <td>(570, 590]</td>\n      <td>108.0</td>\n      <td>225.0</td>\n      <td>110.0</td>\n      <td>233.0</td>\n      <td>303</td>\n      <td>112</td>\n      <td>0.480000</td>\n      <td>0.472103</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>2024.03.18~03.24</td>\n      <td>(590, 610]</td>\n      <td>71.0</td>\n      <td>166.0</td>\n      <td>72.0</td>\n      <td>187.0</td>\n      <td>249</td>\n      <td>95</td>\n      <td>0.427711</td>\n      <td>0.385027</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>2024.03.18~03.24</td>\n      <td>(610, 630]</td>\n      <td>36.0</td>\n      <td>131.0</td>\n      <td>38.0</td>\n      <td>149.0</td>\n      <td>225</td>\n      <td>79</td>\n      <td>0.274809</td>\n      <td>0.255034</td>\n    </tr>\n    <tr>\n      <th>166</th>\n      <td>2024.03.18~03.24</td>\n      <td>(630, 650]</td>\n      <td>17.0</td>\n      <td>142.0</td>\n      <td>15.0</td>\n      <td>150.0</td>\n      <td>227</td>\n      <td>68</td>\n      <td>0.119718</td>\n      <td>0.100000</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>2024.03.18~03.24</td>\n      <td>(650, 1000]</td>\n      <td>18.0</td>\n      <td>68.0</td>\n      <td>18.0</td>\n      <td>77.0</td>\n      <td>93</td>\n      <td>29</td>\n      <td>0.264706</td>\n      <td>0.233766</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>2024.03.25~03.31</td>\n      <td>(0, 530]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>2024.03.25~03.31</td>\n      <td>(530, 550]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>2024.03.25~03.31</td>\n      <td>(550, 570]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>57</td>\n      <td>25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>2024.03.25~03.31</td>\n      <td>(570, 590]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>133</td>\n      <td>60</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>2024.03.25~03.31</td>\n      <td>(590, 610]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>145</td>\n      <td>66</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>2024.03.25~03.31</td>\n      <td>(610, 630]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>170</td>\n      <td>63</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>2024.03.25~03.31</td>\n      <td>(630, 650]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>120</td>\n      <td>49</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>2024.03.25~03.31</td>\n      <td>(650, 1000]</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>73</td>\n      <td>24</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_pd_show()\n",
    "group = ['loan_week',\"score_bin\"]\n",
    "group_calc(data_df, group, sum_col, count_col, unique_col, rate_tupes).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型上线前准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:23:36.563447Z",
     "start_time": "2024-03-28T12:23:36.560440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['L4_r_level_app_package_ratio',\n 'L3_r_level_app_package_ratio',\n 'L1_r_level_app_package_ratio',\n 'L2_r_level_app_package_ratio',\n 'financial_non_system_app_package_ratio',\n 'financial_app_package_ratio',\n 'non_system_app_package_ratio',\n 'wallet_sub_non_system_app_package_ratio',\n 'system_app_package_count',\n 'bank_sub_app_package_ratio',\n 'creditcard_sub_app_package_ratio']"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "for col in feature_select_result:\n",
    "    if col in applist_features:\n",
    "        arr.append(col)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:23:39.049553Z",
     "start_time": "2024-03-28T12:23:39.046641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['loanBillsLoanAvgAmount',\n 'last1EndBillsLoanAmount',\n 'last1LoanBillsLoanAmount',\n 'lastAllAvgOverdueDays',\n 'lastEndLoanDeltaDays']"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = []\n",
    "for col in feature_select_result:\n",
    "    if col in urule_features:\n",
    "        arr.append(col)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:23:39.930375Z",
     "start_time": "2024-03-28T12:23:39.927134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['L4_r_level_app_package_ratio',\n 'L3_r_level_app_package_ratio',\n 'loanBillsLoanAvgAmount',\n 'L1_r_level_app_package_ratio',\n 'L2_r_level_app_package_ratio',\n 'last1EndBillsLoanAmount',\n 'financial_non_system_app_package_ratio',\n 'financial_app_package_ratio',\n 'non_system_app_package_ratio',\n 'wallet_sub_non_system_app_package_ratio',\n 'last1LoanBillsLoanAmount',\n 'system_app_package_count',\n 'bank_sub_app_package_ratio',\n 'lastAllAvgOverdueDays',\n 'lastEndLoanDeltaDays',\n 'creditcard_sub_app_package_ratio']"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_select_result"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 准备测试用例"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:51:08.022784Z",
     "start_time": "2024-03-28T12:51:08.015291Z"
    }
   },
   "outputs": [],
   "source": [
    "data_df[feature_select_result]  = data_df[feature_select_result].round(6)\n",
    "data_df_new[feature_select_result]  = data_df_new[feature_select_result].round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-28T12:54:45.928910Z",
     "start_time": "2024-03-28T12:54:45.833741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         var   target          bin  negative  positive  total  total_rate  \\\n0  score_bin  def_pd1     (0, 530]        22        68     90    0.012159   \n1  score_bin  def_pd1   (530, 550]        99       214    313    0.042286   \n2  score_bin  def_pd1   (550, 570]       329       497    826    0.111591   \n3  score_bin  def_pd1   (570, 590]       898       808   1706    0.230478   \n4  score_bin  def_pd1   (590, 610]      1090       624   1714    0.231559   \n5  score_bin  def_pd1   (610, 630]      1031       325   1356    0.183194   \n6  score_bin  def_pd1   (630, 650]       721       143    864    0.116725   \n7  score_bin  def_pd1  (650, 1000]       495        38    533    0.072008   \n\n   positive_rate       woe      LIFT        KS        IV  \n0       0.755556 -1.519422  2.058381  0.296373  0.606129  \n1       0.683706 -1.282072  1.862640  0.296373  0.606129  \n2       0.601695 -0.948682  1.639214  0.296373  0.606129  \n3       0.473623 -0.437383  1.290303  0.296373  0.606129  \n4       0.364061  0.012889  0.991821  0.296373  0.606129  \n5       0.239676  0.605830  0.652955  0.296373  0.606129  \n6       0.165509  1.060612  0.450902  0.296373  0.606129  \n7       0.071295  1.962495  0.194230  0.296373  0.606129  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>var</th>\n      <th>target</th>\n      <th>bin</th>\n      <th>negative</th>\n      <th>positive</th>\n      <th>total</th>\n      <th>total_rate</th>\n      <th>positive_rate</th>\n      <th>woe</th>\n      <th>LIFT</th>\n      <th>KS</th>\n      <th>IV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(0, 530]</td>\n      <td>22</td>\n      <td>68</td>\n      <td>90</td>\n      <td>0.012159</td>\n      <td>0.755556</td>\n      <td>-1.519422</td>\n      <td>2.058381</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(530, 550]</td>\n      <td>99</td>\n      <td>214</td>\n      <td>313</td>\n      <td>0.042286</td>\n      <td>0.683706</td>\n      <td>-1.282072</td>\n      <td>1.862640</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(550, 570]</td>\n      <td>329</td>\n      <td>497</td>\n      <td>826</td>\n      <td>0.111591</td>\n      <td>0.601695</td>\n      <td>-0.948682</td>\n      <td>1.639214</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(570, 590]</td>\n      <td>898</td>\n      <td>808</td>\n      <td>1706</td>\n      <td>0.230478</td>\n      <td>0.473623</td>\n      <td>-0.437383</td>\n      <td>1.290303</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(590, 610]</td>\n      <td>1090</td>\n      <td>624</td>\n      <td>1714</td>\n      <td>0.231559</td>\n      <td>0.364061</td>\n      <td>0.012889</td>\n      <td>0.991821</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(610, 630]</td>\n      <td>1031</td>\n      <td>325</td>\n      <td>1356</td>\n      <td>0.183194</td>\n      <td>0.239676</td>\n      <td>0.605830</td>\n      <td>0.652955</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(630, 650]</td>\n      <td>721</td>\n      <td>143</td>\n      <td>864</td>\n      <td>0.116725</td>\n      <td>0.165509</td>\n      <td>1.060612</td>\n      <td>0.450902</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>score_bin</td>\n      <td>def_pd1</td>\n      <td>(650, 1000]</td>\n      <td>495</td>\n      <td>38</td>\n      <td>533</td>\n      <td>0.072008</td>\n      <td>0.071295</td>\n      <td>1.962495</td>\n      <td>0.194230</td>\n      <td>0.296373</td>\n      <td>0.606129</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_new['y_p'] = lgb1.predict_proba(data_df_new[feature_select_result])[:,1] # type: ignore\n",
    "data_df_new['y_p_txt'] = gbm_new.predict(data_df_new[feature_select_result])\n",
    "data_df_new['score_txt'] = data_df_new['y_p_txt'].map(lambda x: round(550 - 60 / np.log(2) * np.log(x / (1 - x)), 0))\n",
    "data_df_new['score_bin'] = pd.cut(data_df_new['score_txt'], [0,530,550,570,590,610,630,650,1000])\n",
    "univerate(data_df_new, 'score_bin', 'def_pd1')"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "                     file_name acq_channel product_code product_set_code  \\\n0  raw_am_1_2023-11-03.parquet        AIMX       AIMX_2             AIMX   \n1  raw_am_1_2023-11-03.parquet        AIMX       AIMX_2             AIMX   \n2  raw_am_1_2023-11-03.parquet        AIMX       AIMX_2             AIMX   \n3  raw_am_1_2023-11-04.parquet        AIMX       APTF_2             APTF   \n4  raw_am_1_2023-11-04.parquet        AIMX       ARPP_2             ARPP   \n\n  product_name device_type              user_id         app_order_id  \\\n0       FinMex         ios  1170205197818318848  1170209278406348800   \n1       FinMex         ios  1169807930724634624  1170188202121023488   \n2       FinMex         ios  1169780487926239232  1170177845231013888   \n3  PrestoFácil         ios  1169807930724634624  1170479324248592384   \n4  RápidoPesos         ios  1169807930724634624  1170461036307206144   \n\n   new_old_user_status                tx_id           loan_time  \\\n0                    1  1170209281380110336 2023-11-03 21:59:10   \n1                    1  1170188209247145984 2023-11-03 20:35:52   \n2                    1  1170177851144982528 2023-11-03 19:54:18   \n3                    1  1170479327453040640 2023-11-04 15:52:11   \n4                    1  1170461040333737984 2023-11-04 14:39:35   \n\n  repayment_date          apply_time      id_card_number phone_number  \\\n0     2023-11-09 2023-11-03 21:53:53  MECL880819MMCLHR05   5638000205   \n1     2023-11-09 2023-11-03 20:30:08  DEGF950124HJCLRR07   3326984849   \n2     2023-11-09 2023-11-03 19:48:59  ROGM880713MBCMNY03   6645824156   \n3     2023-11-10 2023-11-04 15:46:57  DEGF950124HJCLRR07   3326984849   \n4     2023-11-10 2023-11-04 14:34:17  DEGF950124HJCLRR07   3326984849   \n\n   def_pd1  agr_pd1  def_cpd  agr_cpd     sms_upload_time  \\\n0      1.0      1.0      1.0      1.0 2023-11-03 21:38:15   \n1      NaN      1.0      NaN      1.0 2023-11-03 20:11:22   \n2      NaN      1.0      NaN      1.0 2023-11-03 19:46:45   \n3      NaN      1.0      NaN      1.0 2023-11-04 15:43:57   \n4      1.0      1.0      1.0      1.0 2023-11-04 13:59:21   \n\n                                                                 applist sms  \\\n0  https://hxwlzg.finmexx.com/file/download/5e027410-e2b0-4caa-84a0-a...       \n1  https://hxwlzg.finmexx.com/file/download/9c49de92-de9d-4b79-a228-1...       \n2  https://hxwlzg.finmexx.com/file/download/83435bc1-eece-4398-bcfc-9...       \n3  https://hxwlzg.finmexx.com/file/download/49979b99-53d5-49ac-9b1f-3...       \n4  https://hxwlzg.finmexx.com/file/download/ad3d3405-d606-411f-a73f-1...       \n\n                                                             device_info  \\\n0  {\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...   \n1  {\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...   \n2  {\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...   \n3  {\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...   \n4  {\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...   \n\n   applist_check  sms_check  \\\n0           True      False   \n1           True      False   \n2           True      False   \n3           True      False   \n4           True      False   \n\n                                                            applist_data  \n0  [{\"app_name\":\"SoftwareUpdateUIService\",\"app_package\":\"com.apple.su...  \n1  [{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...  \n2  [{\"app_name\":\"Uber\",\"app_package\":\"com.ubercab.UberClient\",\"app_ve...  \n3  [{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...  \n4  [{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>file_name</th>\n      <th>acq_channel</th>\n      <th>product_code</th>\n      <th>product_set_code</th>\n      <th>product_name</th>\n      <th>device_type</th>\n      <th>user_id</th>\n      <th>app_order_id</th>\n      <th>new_old_user_status</th>\n      <th>tx_id</th>\n      <th>loan_time</th>\n      <th>repayment_date</th>\n      <th>apply_time</th>\n      <th>id_card_number</th>\n      <th>phone_number</th>\n      <th>def_pd1</th>\n      <th>agr_pd1</th>\n      <th>def_cpd</th>\n      <th>agr_cpd</th>\n      <th>sms_upload_time</th>\n      <th>applist</th>\n      <th>sms</th>\n      <th>device_info</th>\n      <th>applist_check</th>\n      <th>sms_check</th>\n      <th>applist_data</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>raw_am_1_2023-11-03.parquet</td>\n      <td>AIMX</td>\n      <td>AIMX_2</td>\n      <td>AIMX</td>\n      <td>FinMex</td>\n      <td>ios</td>\n      <td>1170205197818318848</td>\n      <td>1170209278406348800</td>\n      <td>1</td>\n      <td>1170209281380110336</td>\n      <td>2023-11-03 21:59:10</td>\n      <td>2023-11-09</td>\n      <td>2023-11-03 21:53:53</td>\n      <td>MECL880819MMCLHR05</td>\n      <td>5638000205</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2023-11-03 21:38:15</td>\n      <td>https://hxwlzg.finmexx.com/file/download/5e027410-e2b0-4caa-84a0-a...</td>\n      <td></td>\n      <td>{\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>[{\"app_name\":\"SoftwareUpdateUIService\",\"app_package\":\"com.apple.su...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>raw_am_1_2023-11-03.parquet</td>\n      <td>AIMX</td>\n      <td>AIMX_2</td>\n      <td>AIMX</td>\n      <td>FinMex</td>\n      <td>ios</td>\n      <td>1169807930724634624</td>\n      <td>1170188202121023488</td>\n      <td>1</td>\n      <td>1170188209247145984</td>\n      <td>2023-11-03 20:35:52</td>\n      <td>2023-11-09</td>\n      <td>2023-11-03 20:30:08</td>\n      <td>DEGF950124HJCLRR07</td>\n      <td>3326984849</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>2023-11-03 20:11:22</td>\n      <td>https://hxwlzg.finmexx.com/file/download/9c49de92-de9d-4b79-a228-1...</td>\n      <td></td>\n      <td>{\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>[{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>raw_am_1_2023-11-03.parquet</td>\n      <td>AIMX</td>\n      <td>AIMX_2</td>\n      <td>AIMX</td>\n      <td>FinMex</td>\n      <td>ios</td>\n      <td>1169780487926239232</td>\n      <td>1170177845231013888</td>\n      <td>1</td>\n      <td>1170177851144982528</td>\n      <td>2023-11-03 19:54:18</td>\n      <td>2023-11-09</td>\n      <td>2023-11-03 19:48:59</td>\n      <td>ROGM880713MBCMNY03</td>\n      <td>6645824156</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>2023-11-03 19:46:45</td>\n      <td>https://hxwlzg.finmexx.com/file/download/83435bc1-eece-4398-bcfc-9...</td>\n      <td></td>\n      <td>{\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>[{\"app_name\":\"Uber\",\"app_package\":\"com.ubercab.UberClient\",\"app_ve...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>raw_am_1_2023-11-04.parquet</td>\n      <td>AIMX</td>\n      <td>APTF_2</td>\n      <td>APTF</td>\n      <td>PrestoFácil</td>\n      <td>ios</td>\n      <td>1169807930724634624</td>\n      <td>1170479324248592384</td>\n      <td>1</td>\n      <td>1170479327453040640</td>\n      <td>2023-11-04 15:52:11</td>\n      <td>2023-11-10</td>\n      <td>2023-11-04 15:46:57</td>\n      <td>DEGF950124HJCLRR07</td>\n      <td>3326984849</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>2023-11-04 15:43:57</td>\n      <td>https://hxwlzg.finmexx.com/file/download/49979b99-53d5-49ac-9b1f-3...</td>\n      <td></td>\n      <td>{\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>[{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>raw_am_1_2023-11-04.parquet</td>\n      <td>AIMX</td>\n      <td>ARPP_2</td>\n      <td>ARPP</td>\n      <td>RápidoPesos</td>\n      <td>ios</td>\n      <td>1169807930724634624</td>\n      <td>1170461036307206144</td>\n      <td>1</td>\n      <td>1170461040333737984</td>\n      <td>2023-11-04 14:39:35</td>\n      <td>2023-11-10</td>\n      <td>2023-11-04 14:34:17</td>\n      <td>DEGF950124HJCLRR07</td>\n      <td>3326984849</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>2023-11-04 13:59:21</td>\n      <td>https://hxwlzg.finmexx.com/file/download/ad3d3405-d606-411f-a73f-1...</td>\n      <td></td>\n      <td>{\"SIM_state\":\"\",\"advertising_id\":\"\",\"background_recovery_times\":1,...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>[{\"app_name\":\"Drive\",\"app_package\":\"com.google.Drive\",\"app_version...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-28T12:57:54.197861Z",
     "start_time": "2024-03-28T12:57:54.188842Z"
    }
   },
   "execution_count": 82
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "case_columns = ['app_order_id','tx_id','applist_data','req_data','sms_upload_time','y_p_txt','score_txt'] +feature_select_result\n",
    "data_df_new[case_columns].sample(100).to_parquet('scorev14_100case.parquet',compression='zstd')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T07:06:19.951160Z",
     "start_time": "2024-03-30T07:06:19.930466Z"
    }
   },
   "execution_count": 35
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "recovery_obj = json.loads(data_df_new[data_df_new['app_order_id']==1172276762487738368]['req_data'].values[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T07:53:23.079168Z",
     "start_time": "2024-03-30T07:53:23.075542Z"
    }
   },
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'loanBillsLoanAvgAmount'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[42], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mrecovery_obj\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mloanBillsLoanAvgAmount\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\n",
      "\u001B[0;31mKeyError\u001B[0m: 'loanBillsLoanAvgAmount'"
     ]
    }
   ],
   "source": [
    "recovery_obj['loanBillsLoanAvgAmount']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T07:53:52.765647Z",
     "start_time": "2024-03-30T07:53:52.753053Z"
    }
   },
   "execution_count": 42
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'curProLastAllAvgEndBillsOverdueDays' in recovery_obj"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-30T07:54:12.830619Z",
     "start_time": "2024-03-30T07:54:12.827444Z"
    }
   },
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
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   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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